Mobility and Transport Connectivity Series Transformative Technologies in Transportation Global Report Wenxin Qiao and Cecilia Briceno-Garmendia © 2024 World Bank International Bank for Reconstruction and Development/The World Bank 1818 H Street NW, Washington DC 20433 Internet: http://www.worldbank.org/transport Standard Disclaimer This work is a product of the staff of The International Bank of Reconstruction and Development/ World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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Cover image: Wenxin Qiao Transformative Technologies in Transportationiii Contents Acknowledgments�����������������������������������������������������������������������������������������������������������������������������������������vii Foreword..........................................................................................................................................................viii About the Authors�������������������������������������������������������������������������������������������������������������������������������������������ix Abbreviations���������������������������������������������������������������������������������������������������������������������������������������������������x Executive Summary.......................................................................................................................................xiii Chapter 1: Overview of Transformative Technologies in Transportation............................................... 1 Chapter at a Glance................................................................................................................................................. 3 Context....................................................................................................................................................................... 4 The Four Entities in the Transportation Network............................................................................................ 9 The Four Categories of Technological Innovations in the Transportation Sector................................... 11 Organization of the Report.................................................................................................................................. 14 References............................................................................................................................................................... 15 Chapter 2: Smart and Sustainable Infrastructure: Unlocking the Power of Digital Platforms.........16 Chapter at a Glance............................................................................................................................................... 18 Context..................................................................................................................................................................... 19 Conventional Intelligent Transportation Systems and Their Evolution....................................................20 Emerging Intelligent Transportation Systems and Their Applications.....................................................24 Smart Mobility Systems.................................................................................................................................25 Smart Traffic Signals......................................................................................................................................28 Smart Transit Systems................................................................................................................................... 31 Smart Asset Management.............................................................................................................................33 Equitable Pricing System................................................................................................................................34 AI Deployment in Transportation.................................................................................................................35 Policy Implications.................................................................................................................................................37 References...............................................................................................................................................................40  merging Passenger Mobility Trend - Connected, Autonomous, Shared, Chapter 3: E and Electric (CASE).................................................................................................................... 44 Chapter at a Glance...............................................................................................................................................46 Context..................................................................................................................................................................... 47 Technology Trends.................................................................................................................................................49 Connectivity through Personal Communication Devices and Telemobility........................................49 Autonomous Vehicles......................................................................................................................................53 Shared Mobility.................................................................................................................................................59 Electrification.....................................................................................................................................................71 Policy Implications.................................................................................................................................................73 References...............................................................................................................................................................76 Transformative Technologies in Transportationiv Chapter 4: Emerging Technology Adoption in Freight Transportation..................................................81 Chapter at a Glance...............................................................................................................................................83 Context.....................................................................................................................................................................84 Types of Freight Innovation................................................................................................................................ 86 Conveyance Innovations................................................................................................................................ 86 Guideway Innovation...................................................................................................................................... 86 Node Innovations..............................................................................................................................................87 Process Innovations.........................................................................................................................................87 Framework for Assessing Technology Adoption in Freight Transportation........................................... 90 Assessment of Technology Innovation in Freight Transportation.............................................................92 Digitization of Freight......................................................................................................................................92 Automation in Freight-handling....................................................................................................................94 Electric Trucks...................................................................................................................................................95 Autonomous Trucking.................................................................................................................................... 96 Drones.................................................................................................................................................................97 Internet of Things............................................................................................................................................ 99 Blockchain......................................................................................................................................................... 99 Next-Generation Air Control........................................................................................................................ 101 Advanced Train Control System.................................................................................................................102 Zero-Carbon Bunker Fuels - Green Ammonia-powered Ships.............................................................102 Policy Implications...............................................................................................................................................103 References.............................................................................................................................................................105 Chapter 5: Enabling Policy Support for Technology Adoption - Connected Institutions.................. 107 Chapter at a Glance.............................................................................................................................................109 Context................................................................................................................................................................... 110 Public Policy Objectives...................................................................................................................................... 110 Public Policy Strategies....................................................................................................................................... 111 Promotional Strategies................................................................................................................................. 113 Regulatory Strategies....................................................................................................................................114 Financial Strategies....................................................................................................................................... 115 Platform Strategies....................................................................................................................................... 116 Connected and Collaborative Institutions.....................................................................................................120 Collaborative Decision-making Structures..............................................................................................120 CDM and Transformative Technologies in Transportation...................................................................121 Policy Implications...............................................................................................................................................123 References.............................................................................................................................................................125 Transformative Technologies in Transportationv Annexures����������������������������������������������������������������������������������������������������������������������������������������������������� 128 Annex 1: Existing and Emerging Shared Modes Concepts and Examples...............................................129 Annex 2: Policy Evaluation Framework for Passenger Mobility Access..................................................132 Annex 3: Methodological Framework for Evaluating the Impacts of CAV Technology.......................134 Annex 4: Application and Impact of Artificial Intelligence in Transportation.......................................142 Annex 5: Technology Investment in Transportation Projects at the World Bank................................148 List of Tables Table 2.1. Summary of Sensors Commonly Equipped for Transportation Infrastructure������������23 Table 2.2. Examples of Traffic and Travel Demand Management Systems�������������������������������������24 Table 4.1. Assessment of Technology Innovation: Digitization of Freight����������������������������������������93 Table 4.2. Assessment of Technology Innovation: Automation in Freight-Handling���������������������95 Table 4.3. Assessment of Technology Innovation: Electric Trucks�����������������������������������������������������95 Table 4.4. Assessment of Technology Innovation: Autonomous Trucking��������������������������������������� 96 Table 4.5. Assessment of Technology Innovation: Drones��������������������������������������������������������������������97 Table 4.6. Assessment of Technology Innovation: Internet of Things���������������������������������������������� 99 Table 4.7. Assessment of Technology Innovation: Blockchain��������������������������������������������������������� 100 Table 4.8. Assessment of Technology Innovation: Next-Generation Air Control�������������������������� 101 Table 4.9. Assessment of Technology Innovation: Advanced Train Control System�������������������102 Table 4.10. Assessment of Technology Innovation: Zero-Carbon Bunker Fuels - Green Ammonia powered Ships����������������������������������������������������������������������������������������������103 Table 5.1. Public Policy Strategies Framework Sorted by Illustrative Applications��������������������� 113 Table A.1. Examples of Fleet Sharing Service Providers in Developing Countries������������������������� 131 Table A.2. Examples of Ride Services in Developing Countries���������������������������������������������������������� 131 Table B.1. Policy Evaluation Framework for Passenger Mobility Access Barriers������������������������132 Table C.1. Summary of CAV Performance Measures���������������������������������������������������������������������������140 Table D.1. Three Approaches of AI Deployment�������������������������������������������������������������������������������������144 Table E.1. Technology Investment in Transportation Projects at the World Bank����������������������148 List of Figures Figure 2.1. Architecture of a Smart Road System�����������������������������������������������������������������������������������22 Figure B2.1.1. Bus Priority Lane and Traffic Control Center, Wuhan�������������������������������������������������������� 27 Figure B2.1.2. Stylized Scheme of Wuhan Transport Planning and Policy Support Center����������������� 27 Figure B2.2.1. Infrastructure for the Cooperative-ITS����������������������������������������������������������������������������������29 Figure B2.2.2. Three-step Plan to Modernize São Paulo’s Traffic Lights System��������������������������������� 30 Figure B2.3.1. Automated Transit Fare Systems in African Cities������������������������������������������������������������32 Transformative Technologies in Transportationvi Figure 2.2. Counting vehicles, pedestrians, and motors in Rwanda through AI��������������������������������37 Figure 3.1. Society of Automotive Engineers’ Levels of Automation���������������������������������������������������54 Figure 3.2. Innovative/Emerging and Classic Fleet Sharing and Ride/Delivery Services��������������� 60 Comparative Timeline of Key Developments in Shared and Digital Mobility in Figure B3.10.1.  Developed and Developing Countries (2000–20)���������������������������������������������������������������� 69 Figure C.1. Methodological Framework for Evaluating the Strategic Impacts of CAV Technology��������������������������������������������������������������������������������������������������������������������136 Figure C.2. Conceptual Framework of the Performance Simulation Component���������������������������139 List of Boxes Box 2.1. The Evolution of ITS Investments in Wuhan City, China�����������������������������������������������������������26 Box 2.2. Smart Signals: The Traffic Lights Systems with 5G in São Paulo, Brazil�����������������������������29 Box 2.3. Innovation in Fare Collection Systems for Public Transportation in African Cities������������32 Box 2.4. Five Areas that AI May Transform Transportation��������������������������������������������������������������������36 Box 3.1. Connected Vehicle System��������������������������������������������������������������������������������������������������������������� 51 Box 3.2. Internet of Things and Connected Cities���������������������������������������������������������������������������������������52 Box 3.3. Advanced Air Mobility in India���������������������������������������������������������������������������������������������������������59 Box 3.4. Carsharing Programs Across Countries���������������������������������������������������������������������������������������� 61 Box 3.5. Shared Micromobility Across Countries����������������������������������������������������������������������������������������62 Box 3.6. Shared Ride and Delivery Services Across Countries�����������������������������������������������������������������64 Box 3.7. Gender Concerns in Public Transportation and Shared Mobility���������������������������������������������65 Box 3.8. Informal Shared Ride Services Across Countries����������������������������������������������������������������������� 66 Box 3.9. The Growth of “Super Apps” in Developing Countries��������������������������������������������������������������� 68 Box 3.10. Similarities and Differences of Shared Mobility between Developed and Developing Countries���������������������������������������������������������������������������������������������������������������� 69 Box 3.11. Electrification of Public Transport: A Case Study of the Shenzhen Bus Group������������������� 72 Box 4.1. Rivigo’s RaaS System in India�������������������������������������������������������������������������������������������������������� 88 Box 4.2. Economic Impact of Freight Transportation in the Context of Land-locked and Developing Countries����������������������������������������������������������������������������������������� 91 Box 4.3. Digital Freight Platforms Leapfrogging in Africa������������������������������������������������������������������������94 Box 4.4. Drone Delivery������������������������������������������������������������������������������������������������������������������������������������� 98 Box 4.5. Blockchain in Freight����������������������������������������������������������������������������������������������������������������������� 100 Box 5.1. Dynamic Toll Lanes Offers Social and Environmental Benefits��������������������������������������������� 116 Transformative Technologies in Transportationvii Acknowledgments This report has been prepared by a core team led by Wenxin Qiao (Senior Transport Specialist) and Cecilia Briceno-Garmendia (Global Lead Economist) of the World Bank’s Transport Global Practice, under the guidance of Nicolas Peltier-Thiberge (Global Director, Transport Practice) and Binyam Reja (Global Practice Manager, Transport Practice). Other contributing members of the team include Cecilia Fabian Kadeha, Rebekka Bellmann, Fernanda Ruiz Nunez, Tania Priscilla Begazo Gomez, Jai Malik, Alejandro Molnar, Catalina Ochoa, Azeb Afework, and Licette Moncayo. This report benefited from background research from a diverse group of experts. Special acknowledgment goes to Hani Mahmassani (Northwestern University) and Shanjiang Zhu (George Mason University) for their key contribution to the report. Other main contributing authors for specific topics are Susan Shaheen and Adam Cohen (University of California, Berkeley), Chris Caplice and Jarrod Goentzel (Massachusetts Institute of Technology), David Levinson (University of Sydney), Xianfeng Yang (University of Maryland), Giovanni Circella (University of California, Davis), Alyas Abibawa Widita (Monash University), Patricia Mokhtarian (Georgia Institute of Technology) and Yusak Susilo (University of Natural Resources and Life Sciences). The team thanks the peer reviewers for their excellent comments and valuable suggestions during various stages of the report development: Georges Bianco Darido, Adam Stone Diehl, Yang Chen, Carlos Bellas Lamas, Rajendra Singh, Bianca Bianchi Alves, Stephen Muzira, Svetlana Vukanovic, Serene Ho, Ana Waksberg Guerrini, Nupur Gupta, Joanna Charlotte Moody, Gerald Paul Ollivier, Maria Vagliasindi, Tarek Keskes, Carolina Monsalve, Daniel Alberto Benitez, Vivien Foster, Neil Pedersen, Corey Harper, Aurelio Menendez, Claudia Adriazola-Steil, Lori Tavasszy, Lori Pepper, and Philippe Crist. We appreciate the support from our communication team, including Erin Scronce and Xavier Bernard Leon Muller. Jonathan Davidar, from the Knowledge Management team, provided creative direction and oversaw the report production. The report was edited and designed by RRD GO Creative. This report benefited from the financial support from the Digital Development Partnership (DDP) and the Public–Private Infrastructure Advisory Facility (PPIAF), administered by the World Bank. The support from Elena Babkova, Luciana Guimaraes Drummond E. Silva, Bertram Boie, and Jemima Sy are gratefully acknowledged. The team is deeply grateful to the Guangzhe Chen (Vice President, Infrastructure) for his excellent leadership and invaluable guidance. The team also extends its gratitude to the Makhtar Diop (Managing Director and Executive Vice President, International Finance Corporation) for supporting our initial ideas and launching the underlying research project. Transformative Technologies in Transportationviii Foreword Transformative technology is destined to play a critical role towards sustainable, smart, and equitable mobility, to create a world free of poverty on a livable planet. Technologies become transformative when they change the paradigm, allowing us to do new and different things that were not possible previously. Technology innovations have been a transformative force that reshape the transport sector over time. Historically, innovations have been concentrated in vehicle technologies and transportation infrastructure. In the past few decades, the transport sector has started to benefit from technological advances in information and communication technology, which helped transform how mobility services are provided. Door- to-door transport services spanning multiple modes could be available within minutes with a simple swipe of your fingertips on a smartphone screen, be it for a passenger or a parcel. The convergence of technological innovations in digital connectivity, data platforms, automation, and alternative energy is creating exciting changes to our transport system. From mobility to accessibility, sustainability to ecosystem stewardship, regional equity to social justice, economic development to efficient markets, the transport sector is called upon to play a growing and ever more critical role in the sustainable future that we aspire to. And yet, critical questions, such as what makes a technology innovation transformational, how to harness the most benefits and mitigate potential negative impacts, and what are the roles of the public and private sectors in technology adoption have not been sufficiently addressed, particularly in the context of low- and middle-income countries (LMICs). It is essential to understand how mobility can evolve through the influence of technology to meet changing economic requirements and social expectations. Likewise, social and economic forces that may be shaping future travels are increasingly relevant to the discussion of the associated infrastructure. Exciting technological innovations have emerged across the developing world. The stakes of taking the “right” actions are high. Transformative technologies can influence developing countries’ transport service delivery and unlock opportunities for development through enabling transport systems. They can also structure policy responses and interventions by governments and other stakeholders to realize positive dividends from innovation in the sector. This report examines the cutting-edge transformative technologies that enable higher mobility that can be attained at lower costs to the society, while creating new service delivery options. It also investigates their potential contributions to solving existing and future developmental challenges. The report focuses on major participants of the transportation system: infrastructure providers, passengers, freight, and policy makers. Collectively, these materials provide both a panorama of recent technology-driven innovations in the transport sector, and detailed takeaways to advance relevant policy dialogues, supported by examples of World Bank operational engagement in various areas, illustrating relevant investment and collaboration possibilities in LMICs. Although each community will need to find its own path towards technology adoption, this report helps to foster the dialogue within the context of LMICs. Nicolas Peltier-Thiberge Binyam Reja Global Director for Transport Practice, Global Practice Manager for Transport Practice, World Bank World Bank Transformative Technologies in Transportationix About the Authors Wenxin Qiao Wenxin Qiao is a Senior Transport Specialist with the World Bank, working on analyzing transformative technologies and their impact on global transportation networks and supporting improvements toward sustainable and intelligent transportation networks. She specializes in developing network models to optimize traffic conditions and resource use, including prioritizing public transit systems, predicting travel times, and improving network resilience. She has authored or coauthored about 30 peer-reviewed publications in international journals and conferences and has served as peer reviewer for several transportation journals. She holds a Ph.D. in transportation from the University of Maryland. Cecilia Briceno-Garmendia Cecilia Briceno-Garmendia is the Global Lead for Transport Economics, Policy, and Innovation at the World Bank and leads the World Bank’s Transport Decarbonization agenda. She also works with the World Bank’s operational teams in reassessing the lens through which mobility and logistics are incorporated at country and regional levels to capitalize on synergies between climate action and development. She has conducted research and development projects in over 75 countries spanning Africa, Asia and the Pacific Islands, Europe, and Latin America. Previously, she worked in software engineering, specializing in artificial intelligence and the design of information and organizational systems. She has a PhD in economics from Georgetown University and an MBA from the Instituto de Estudios Superiores en Administración in Caracas, Venezuela. Transformative Technologies in Transportationx Abbreviations AAM Advanced air mobility AFC Automated fare collection AHS Automated highway system AI Artificial Intelligence AMS Autonomous mobility service APDS Alliance for Parking Data Standards API Application programming interface APTS Advanced public transportation system ARTS Advanced rural transportation system ATC Area traffic control ATCS Advanced train control system ATIS Advanced traveler information system ATMS Advanced traffic management system AV Autonomous vehicle AVCS Advanced vehicle control system AVL Automated vehicle location B2C Business-to-consumer BRT Bus rapid transit BYD Build Your Dream Company Limited CASE Connected, autonomous, shared, and electric CAV Connected and autonomous vehicle CDM Collaborative decision-making C-ITS Cooperative-ITS CNS Courier network services CORSIA Carbon Offsetting and Reduction Scheme for International Aviation CV Connected vehicle DoT Department of Transportation EDI Electronic data interchange ETC Electronic toll collection EV Electric vehicle FAA Federal Aviation Administration FCD Floating car data FCEV Fuel cell EV Transformative Technologies in Transportationxi FEU Forty-foot equivalent unit GBFS General Bikeshare Feed Specification GDP Gross domestic product GHG Greenhouse gas GLOSA Green Light Optimized Speed Advisory GPS Global Positioning System GTFS General Transit Feed Specification GTFS-RT Real-time GTFS HIC High-income country HOV High-occupancy vehicle ICE Internal combustion engine IATA International Air Transport Association ICC Interstate Commerce Commission ICT Information and communication technology IEA International Energy Agency IoT Internet of Things iRAP International Road Assessment Program ITS Intelligent Transportation System LAC Latin America and the Caribbean LDV Light-duty vehicle LiDAR Light detection and ranging LLDC Landlocked developing country LMIC Low- and middle-income country LNG Liquefied natural gas MaaS Mobility-as-a-Service MAASTO Mid-America Association of State Transportation Officials MCA Motor Carrier Act MDS Mobility data specification MIB Management information base ML Machine learning MOD Mobility-on-demand MPO Metropolitan planning organization NABSA North American Bikeshare Association NAHSC National Automated Highway System Consortium NETT Non-traditional and Emerging Transportation Technologies Transformative Technologies in Transportationxii NHTSA National Highway Transportation Safety Administration P2P Peer-to-peer P3 Private-public partnership RaaS Relay-as-a-Service RSU Road-side unit SAV Shared AV SCA South & Central Asia SSA Sub-Saharan Africa SuM4All Sustainability Mobility for All initiative SUV Sport utility vehicles SZBG Shenzhen Bus Group Company TCO Total cost of ownership TEU Twenty-foot equivalent units TFP Total factor productivity TMS Transportation management system TNC Transportation network company TPAS Truck Parking Availability System TPIMS Truck Parking Information Management System TSP Transit signal priority UAS Uncrewed aircraft system UAV Unmanned aerial vehicle UN DESA UN Department of Economic and Social Affairs US DoT U.S. Department of Transportation V2I Vehicle to infrastructure V2V Vehicle to vehicle V2X Vehicle to anything VAT Value-added tax VMS Variable message sign VTOL Vertical take-off and landing WFP World Food Programme WIM Weigh-in-motion Transformative Technologies in Transportationxiii Executive Summary Key Messages and Policy Recommendations Transportation is quickly evolving, adapting, shaping, and being shaped by global megatrends, promoting energy efficiency and environmental quality. Transportation systems enable access to essential services and job opportunities and facilitate the production, trade, and distribution of goods. The transportation infrastructure and services that utilize it are vital to economic prosperity and social well-being, and sustainable and smart mobility is an essential enabler of poverty reduction and shared prosperity. Historically, the rapid expansion of transportation networks has been associated with economic growth and social development. However, it is now widely recognized that infrastructure expansion alone is not sufficient to address contemporary transportation and mobility problems. Equally important is the need to utilize the existing system more efficiently and enable a wide array of mobility solutions and innovative approaches that meet increasingly diverse needs in varying environments. Given the rising levels of congestion, road crashes, local air pollution, energy consumption, and greenhouse gas (GHG) emissions, it is imperative to find a smarter path for future development. To many policy makers and practitioners, technological innovations are the key enablers of such transformation. Technologies become transformative when they change the paradigm, enabling higher mobility levels that can be attained at significantly lower economic and environmental costs. New transportation services and business models are pointing to exciting overarching themes for future transportation systems, making them more user-centric and demand-responsive, as well as safer, greener, and more efficient. The emerging technologies create new opportunities for service delivery options that, in the past, would not have been possible or practical — operationally or economically. Recognizing and leveraging these megatrends, and quickly, effectively, and responsibly adopting context-appropriate transformative technologies could help developing countries achieve economic growth and human development goals in a sustainable and equitable way. The pathway to smart and sustainable mobility is not straightforward. The transportation sector is prone to market failures, and technological innovations are particularly susceptible to this. To navigate through this complex landscape, regulatory agencies need to develop a good understanding of the technological and managerial developments in the transportation sector and make informed decisions to foster successful deployment. The transportation sector is historically subject to government regulation as many services are inherently natural monopolies and they are responsible for various types of externalities – from the negative impact of air pollution and congestion, to the network externalities present in platforms, as well as the moral hazard linked to transportation safety. Deploying transformative technologies in the transportation sector today is even more challenging, as transportation is an essential service that has weaved itself into many other areas, including safety, environmental protection, land use, labor market, trade relations, privacy, data security, public health, and integration with legacy systems. Failing to consider these factors, in addition to controlling other well-known externalities, may lead to failure in delivering the promised benefits. How we manage technological shifts will have profound consequences for the decarbonization of the sector, congestion reduction, and access to social and economic opportunities. Transformative Technologies in Transportationxiv Deploying transformative technologies could play a crucial role in addressing sustainable development challenges, particularly in developing countries. With the rapidly evolving landscape of the modern transportation network, the knowledge gap in how to leverage and discover the potential role of transformative technologies in addressing key development challenges is becoming increasingly evident. Opportunities for transforming the sector through the adoption and deployment of transportation technologies are enormous. However, they may entail a structural change in existing business models. Modern transportation systems are being structurally challenged by new business models as new types of services flourishes, enabled by technological developments, the data and digital revolution, and the social behaviors and trends linked to those. To harness the promised dividends while mitigating potential externalities, a careful planning process is needed, and many issues need to be addressed to ensure equitable and sustainable mobility. Therefore, it is important to identify key barriers for deploying transformative technologies and develop a deployment plan that is well customized for local conditions. Accordingly, the role of the government and regulatory tools also need to adapt so technologies could be efficiently and effectively deployed, and act as a catalyzer for shared prosperity. This report aims to address the existing knowledge gap in understanding how transformative technologies can influence the developing countries’ transportation service delivery, unlock opportunities for development through enabling transportation systems, and structure policy responses and interventions by governments and other stakeholders to realize positive dividends from innovation in the sector. The critical transformative technologies are examined through the lens of the four essential elements in the transportation network: users, vehicles, physical infrastructures, and institutions. The technologies are assessed from the engineering perspective, the economic perspective, and the policy perspective. The discussion focuses on four categories of technologies: connectivity, digital platforms, automation, and alternative energy. The report storyline starts from an overview of transformative technology in transportation sector, covering conventional to emerging intelligent transportation systems (ITS) technologies, then explaining how these technologies are relevant to passenger and freight transportation, and end with discussions on policy and regulatory implications. This policy note discusses the most relevant policy questions regarding the impact of technology innovation and adoption pathways and recommends steps for achieving the potential benefits and transformative potential of the emerging landscape of future mobility. The first five questions aim to provide an overview of the recent technology-driven innovations (“what”) in the transportation sector and “why” they could help developing countries leapfrog toward an efficient, inclusive, sustainable, and equitable transportation system for all. The last five questions focus on “how” and offer readers the most important takeaways to advance relevant policy dialogues. These questions are selected to offer a succinct glance at the most important topics and findings of this report. More comprehensive elaborations as well as detailed examples of World Bank operational engagement in various areas designed to offer both Bank teams and policymakers relevant investment and collaboration possibilities can be found in the full report. Transformative Technologies in Transportationxv Addressing Questions Question 1: What makes a technology innovation transformational in the transportation sector? Technologies become transformative when they change the paradigm, enabling higher mobility levels that can be attained at significantly lower economic and environmental cost. The past decades have witnessed the progressive adoption of information and communication technologies (ICTs) in transportation systems. The first order impact of these technologies is to allow the same processes to be performed better, faster, and cheaper. But the processes and the nature of the service remain essentially the same, until such incremental changes lead to a paradigm shift. When a physical map is replaced with a digital one but used in the same way as a physical map, the impact is incremental in terms of convenience and interactivity. When it is overlaid with real-time travel time prediction, the technology becomes transformative as it enables real-time routing guidance and may change the entire process of planning and traveling. Transportation technologies are transformative when they enable higher levels of mobility for users, at lower environmental and carbon impact, while creating new service delivery options that would not have been possible or practical (operationally or economically) in the past. For any technology to be truly transformational, it must be widely adopted, creating a megatrend that sweeps the industry and society in a relatively short period. To be widely adopted, it needs to have sufficient direct or indirect benefits — it needs to improve system performance in terms of utilization, productivity, and effectiveness, arising from innovations in the conveyances, guideways, nodes, and processes. Additionally, the innovation needs to address specific problems or realize opportunities that exist in the region to be relevant. It must also be compelling, deployable, and affordable. Meanwhile, the supporting systems, including financial, legal, political, educational, and civil infrastructure development, need to enable innovation adoption in the region. Transformative technologies offer developing countries the opportunity to leapfrog traditional development paths and avoid repeating the same mistakes of others. However, some technologies being pursued in other regions may be poor bets for wide adoption in developing countries due to factors such as infrastructure, rule of law, regional regulatory consistency, access to financing, and professional education. Contextual factors may also provide opportunities for some technologies to advance faster in developing countries, leapfrogging the dominant paradigms that have become entrenched in other regions. The key is identifying technologies that address relevant problems, and for which the enabling supporting systems exist for widespread adoption. Therefore, identifying such transformative technologies at the early stage and analyzing them from economic, social, environmental, and policy perspective is important. In this report, Chapter 1 provides a quick overview of technological innovations in the categories of connectivity, digital platform, automation, and alternative energy, while Chapters 2 to 5 elaborate further from the infrastructure, passenger, freight, and institutions’ perspectives. Case examples of World Bank operational engagement in various areas are provided in boxes to showcase relevant investment and collaboration possibilities throughout the report. Transformative Technologies in Transportationxvi Question 2: How can technology improve the efficiency of the transportation systems? Recent technological advancements in vehicle automation, sensing technologies, optimal control systems, clean energy solutions, telecommunication networks, and data analytics have revolutionized the transportation sector, leading to paradigm shifts and the emergence of more efficient and sustainable transportation solutions. Providing mobility has already shifted from an infrastructure supply process to an operation- oriented paradigm, and is now shifting again to a user- and service-centric one. Travelers now have access to real-time information on road travel times, incident locations, weather conditions, road conditions, optimal routes, lane restrictions, transit timetables, and more. This wealth of information enables individuals to make better-informed decisions about their travel plans. Additionally, interactive shared mobility platforms have emerged, offering users convenient and cost-effective on-demand access to various transportation modes. These new services transcend the traditional boundaries set by specific modes or service providers, providing travelers with door-to-door multimodal solutions that better meet their individual needs. Furthermore, ongoing developments in vehicle technology have made vehicles cleaner, smarter, and more connected. Vehicle automation has progressed in phases, enhancing the safety of vehicular trips. Smart vehicles are now capable of communicating with intelligent infrastructure, establishing synergies and opportunities for optimizing the entire transportation system. This connectivity extends beyond vehicles to encompass vulnerable road users, contributing to overall system safety. The rapid advancement of artificial intelligence (AI) in combination with the data platforms enabled by smart vehicles and infrastructure holds great potential for further improving transportation. These technologies provide opportunities for enriching data collection, enhancing mobility, reducing costs, and increasing the reliability and resilience of the transportation system. AI-powered systems can analyze vast amounts of data to optimize traffic flow, manage congestion, and predict demand patterns, leading to more efficient transportation operations. Additionally, the integration of AI and data analytics can enable predictive maintenance, improving the reliability of vehicles and infrastructure while minimizing disruptions. The transformative effects of these emerging trends extend far beyond the transportation sector. They have the potential to reshape various aspects of everyday life, including relieved time from driving, and have reshaped the dynamics of supply chains. As these innovations continue to evolve, they hold the promise of creating a more efficient, sustainable, and interconnected transportation system that benefits individuals, communities, and the environment. Question 3: How can technology help support the decarbonization of the sector? The prevailing view in transportation economics has long been that a stronger economy and a higher quality of life lead to higher demand for mobility and a larger environmental impact. However, technological innovations are challenging this conventional wisdom and providing us with the tools to create a transportation system that addresses both economic growth and environmental concerns. Technology serves as the enabler for sustainable transportation by providing solutions of better planning, management, operations, and maintenance. Embracing these technological innovations is crucial for decarbonizing the transportation sector. Transformative Technologies in Transportationxvii One of the key pathways is through the optimization of travel decisions. With the advent of advanced data analytics, real-time information, and smart mobility platforms, individuals can make more informed choices about their travel routes, modes of transportation, and timing for departure. By selecting the most efficient and environmentally friendly options, such as public transit, shared mobility services, or active transportation modes, technology empowers users to reduce their carbon footprint while still meeting their mobility needs. Technological advancements also play a transformative role in logistics and supply chain management. Through improved planning, route optimization, and real-time tracking, technology enhances the efficiency of freight transportation, reducing energy consumption and emissions associated with the movement of goods. Furthermore, technologies like blockchain enable increased transparency and accountability in supply chains, facilitating the adoption of sustainable practices and supporting the traceability of environment-friendly products. As the transportation sector currently accounts for 20 percent of all GHG emissions, decarbonization becomes a prominent environmental issue that the sector needs to address. Electrification of mobility is gaining significant momentum across several major global markets and mainly as an instrument to decouple mobility and growth, from both local and global emissions. Electric vehicles (EVs) offer a major energy advantage due to the efficiency of engines. On average, petrol vehicles consume four to five times as much energy per vehicle-kilometer as EVs. The energy advantage tends to be larger for LMICs with old and relatively inefficient internal combustion engine (ICE) fleets, and the adoption of EVs can bring GHG reduction even before the power grid is fully decarbonized. The integration of renewable energy sources into the charging infrastructure, such as solar and wind, further reduces the carbon intensity of electric mobility. However, EV transition is not a panacea to achieve sustainable mobility, which should be embedded in broader sustainable transportation strategies such as the avoid-shift-improve paradigm. It is worth noting that electrification is not the only alternative to improve energy efficiency and decarbonize the transportation sector. While the EVs based on lithium battery are popular in the market, the development and deployment of fuel cell EVs (FCEVs) based on hydrogen is also making progress. As the manufacturing and operation costs drop further, the uptake of FCEVs may accelerate, particularly in the long-haul freight market. This also applies to other transportation modes. The use of sustainable aviation fuel, a renewable or waste-derived aviation fuel, is a carbon reduction pathway and can reduce aviation GHG emissions. Biofuels, hydrogen and ammonia, and synthetic carbon-based fuels are also promising alternatives toward the zero-carbon objective in maritime transportation. Therefore, electrification should be considered in the broader context of zero-emission energy alternative and may be complemented by other energy sources in different niche markets. Furthermore, technology-driven innovations in vehicle automation and connectivity have the potential to optimize traffic flow, reduce congestion, and minimize fuel consumption. Connected vehicles (CVs) play a crucial role in this transformation by enriching real-time data collection and enabling better traffic management decisions. Through their ability to communicate with both vehicles and infrastructure, CVs facilitate more energy-efficient vehicle trajectory control, known as eco-driving. Another significant contribution comes from autonomous vehicles (AVs). With their advanced sensing and decision-making capabilities, AVs can navigate through traffic more efficiently, reducing unnecessary idling, aggressive driving, and stop-and-go patterns that lead to higher fuel consumption Transformative Technologies in Transportationxviii and emissions. Especially, when AVs are deployed in shared mobility services, the shared AVs have the capacity to maximize vehicle utilization and reduce the overall number of vehicles on the road, hence significantly decreasing congestion and the associated environmental impacts. Question 4: How can technology help ensure a just transition toward climate-smart transportation systems? In LMICs, motorization may contribute to inequity and disparities, resulting in the “Green Divide”. This trend is driven by an increase in motorization in developing countries and more rapid urbanization. Without aggressive actions, transportation emissions are expected to continue growing, particularly in developing countries. In many cases, technology-related transportation investment may generate additional social benefits beyond the intended transportation services. Some of these benefits are also apparent based on early adoption examples in developed countries; others may be related to unique conditions in developing countries. Examples of how some projects managed to harness these additional benefits can inspire other potential adopters to gain support from more stakeholders and thus, speed up the adoption. Although the full extent of the benefits associated with smart infrastructure is difficult to quantify and might not be directly reflected, the benefits extend beyond mobility improvement and encompass social inclusion, safety, and economic and environmental externalities, among others. The use of shared and on-demand mobility in developing economies has the potential to expand access to jobs and critical services, while providing new and growing employment opportunities in the sector. However, it is crucial to address disparities and ensure that these initiatives provide fair employment opportunities and promote upward mobility, particularly for workers without formal job training. Smart infrastructure needs the support of physical equipment (for example, ICTs), intelligence through analytics, optimization, and AI capabilities, as well as skills from well-trained technical and management teams. Governments can customize and prioritize components based on local conditions and expand the capacity of the system incrementally. Enhancing transportation system resilience is a benefit of ITS that may have been previously ignored but is increasingly attracting attention. Enhancing infrastructure climate resilience has become an important objective of many infrastructure projects. Digitization of transport asset management systems helps many countries better prepare themselves for natural disasters and emergency responses. The transportation sector is a natural ground for leveraging existing infrastructures and applying AI technologies. AI can bring new opportunities to the industry, create new job opportunities in developing countries and present them with a possible avenue to meet future smart mobility needs by better utilizing the capacity of existing infrastructure. AI may also bring significant impact to the economy and reshape the transportation labor market in developing countries. However, to ensure an equitable growth, the public sector must proactively implement policies that address disparities in transportation services. Identifying, understanding, and resolving user and labor equity challenges are essential for ensuring accessibility, affordability, and job opportunities for all. In the context of shared mobility, achieving social equity for users involves addressing disparities in affordability, predictability, availability, accessibility, and technological proficiency. The policy Transformative Technologies in Transportationxix framework discussed in this report can help identify mobility challenges and opportunities and navigate through spatial, temporal, economic, equity, and social changes and provide policy potential to address different mobility needs. The impacts of shared mobility deployments in developing countries will almost certainly vary and policies should be tailored to local social, economic, urban planning, and governance contexts. However, the government’s commitment to “Leaving No One Behind” in the process should be clear and strong. A “Just Transition for All” in the technology adoption process needs to be people and communities centered. Policymakers should work with stakeholders to create an enabling environment to mitigate environmental impacts, support impacted people, and build a renewable energy future when adopting new technologies in the transportation sector. Appropriate policy initiatives from the public sector could send a clear message to the public and the industry, as discussed in Chapter 3, and lead to sustainable and equitable deployments in developing countries. Question 5: Why do transformative technologies in the transportation sector matter to development, and how can developing counties get ready for the technology adoption and leapfrog? In transportation, we observe that every country is developed, and every country is developing. Developing countries should exploit the opportunities to leapfrog the historic transportation development processes of developed countries. Whether this is using digital money for payments, sharing services, electrifying the fleet before the wide adoption of the ICE vehicles, or using aerial drones for medicine delivery rather than waiting for highways to be built and trucks to run, there are numerous ways to accelerate development of essential mobility services. The stakes of taking the “right” actions to leapfrog in the transportation sector and in human development are high for developing countries seeking opportunities to enhance mobility and raise their residents’ quality of life. Technology adoption and deployment is not a one-size-fits- all proposition; different countries may follow different adoption and adaption paths. This is particularly true for developing countries that have not gone through the endless cycle of building more roads and inviting more congestion. The fact that emerging technologies place most of the functionality and intelligence with individuals (via mobile phones and wireless networks) and vehicles (rather than relying on costly physical infrastructure) suggests potential for leapfrog opportunities in developing countries that may not have acquired the physical transportation infrastructure to meet their mobility needs, as discussed in Chapter 2. Countries need to choose a suitable technological pathway that aligns with their existing infrastructure conditions. For instance, the widespread adoption of EVs requires access to charging stations, a stable power network, and sufficient generation capacity, which may not be guaranteed in many developing countries. Similarly, the successful implementation of new mobility services requires convenient, continuous, and affordable access to wireless communication network. However, access to digital infrastructure and connectivity remains severely limited in some developing countries, which prevents them from quickly adopting technological innovations and harnessing its benefits. Therefore, it is important to identify key barriers to deploying transformative technologies and develop a deployment plan that is well customized for local conditions. Developing countries are readily deploying innovations in shared and on-demand mobility, generally provided by an entrepreneurial private sector. Carsharing has the potential to be an attractive Transformative Technologies in Transportationxx option for vehicle access in developing countries where auto ownership can be cost prohibitive. The peer-to-peer (P2P) model can also be an attractive option for vehicle owners seeking to offset the high costs of automobile ownership by renting out their vehicles when not being used by the primary household. In some regions, the availability of low-cost labor enables the delivery and pickup of carsharing vehicles, a new application of the one-way service model in developing countries, as discussed in Chapter 3. A key similarity between shared mobility in developed and developing countries is the desire to integrate trip planning and fare payment onto a single digital platform. Indeed, some developing countries seem to be leapfrogging into “super apps” with an array of transportation, retail, lifestyle, and other services aggregated onto a single app-based platform, especially advanced in the features and level of sophistication of app-based mobility services. This is another example where enabling an entrepreneurial private sector is adding value in a space historically controlled by a public sector. Therefore, developing countries should take this opportunity to improve the social welfare given that certain policies, regulations, and public outreach are needed to ensure a healthy deployment of AI in transportation. The cost of deploying AI in transportation is much less than the cost of expanding infrastructures. But the benefits may be comparable in certain situations. Deploying an AI developed by a third party and improving AI performance with new data is often the cost-effective approach for developing countries. Governmental transportation agencies need to identify the use cases and the deployment is often accomplished by the collaboration of the government and private sectors. In developed countries, the focus of ITS applications is also evolving. Although the investment in conventional ITS applications such as traffic data collection at fixed locations, actuated signal control system, and traveler information system based on variable message signs is still ongoing, technologies such as connected and automated vehicles are emerging rapidly. However, the adoption of pathways in developing countries may vary. In telecommunication, many developing countries skipped the phase of landline-based technologies (for example, dial-up internet connection), to directly build out their mobile networks. Similar leapfrog opportunities may also emerge in the transportation sector. In such a scenario, developing countries may face fewer challenges related to a huge legacy infrastructure system or calcified regulatory framework, but may need to address issues such as a lack of essential infrastructure, technology support, and skilled workforce. Case studies on early adopters in developing countries may offer valuable experience to other countries that aspire to explore such opportunities. Incorporating transformative technologies into transportation systems entails fostering an environment that encourages innovation, investment, and experimentation to explore the full potential of better mobility services. Developing countries can seize the opportunity to leapfrog traditional development paths and create transportation systems that not only meet their current needs but also pave the way for a sustainable and inclusive future. Chapter 5 provides a glance at the past successes and failures of technology deployment in transportation and discusses the key enabling policies. Transformative Technologies in Transportationxxi Question 6: What are the roles of the public and private sectors in technology adoption, and how can governments create an enabling environment for the adoption of technologies? This report identifies four primary policy objectives for transportation systems: enhance economic efficiency, increase equity, reduce negative externalities, and improve the user experience. To achieve these objectives, governments have the policy strategies of promotional, regulatory, financial and platform strategies, that enable them to implement change and pursue their policy goals. The public sector’s primary contribution is to consistently provide enabling financial, legal, political, educational, and civil infrastructure systems. Their incentives consist of both direct and indirect benefits that might be transformative in the long term, such as increased trade, equitable access to goods, economic development, better resilience, and lower environmental impact. The alignment of short- and long-term incentives across the public and private sectors is required to motivate the adjacent steps on a path to widespread adoption and transformative innovation. On the other hand, the private sector’s primary contribution will be to creatively connect innovations with relevant problems. Their incentives are direct financial benefits in the short term, such as cost reduction, revenue growth, and asset productivity. The public sector has traditionally been responsible for transportation infrastructure provision and ITS-related investment decisions. However, the landscape has been evolving, with innovations from the private sector playing an increasingly prominent role in technology development and deployment. The development of smart infrastructure requires a comprehensive review of sensing, processing, and implementation capabilities. Interinstitutional collaboration is essential to facilitate deployment and unlock the full potential of transformative technologies. Given limited resources, intersectoral coordination also becomes crucial at different stages such as planning, development, operations, and maintenance. In the context of urban mobility, infrastructure deployments must integrate road transportation infrastructure with telecoms and smart grid systems. Additionally, new software platforms are needed to support CVs and smart cities, as their absence currently poses a significant deployment bottleneck. The emergence of new technologies offers opportunities to establish new, potentially more effective models of ownership and operation, reflecting more flexible forms of service delivery through increased involvement of the private sector and public- private agreements. The opportunity lies in leveraging the technology and financial capabilities of the private sector to provide mobility services at a scale and quality that exceed what might be attainable under conventional public sector resources, programs, and capabilities. The risks lie in precluding the development of a capable and progressive-looking public sector, and in solutions that might leave behind economically disadvantaged communities. It is also essential for collaborative policymaking to involve all relevant parties, so that regulations can be developed in a manner that mitigates concerns about disruptions and garners support from important players. This collaborative approach becomes crucial in situations where existing operators and stakeholders fear the potential impacts of new regulations. Policy makers must strive to develop regulations that consider the interests of all stakeholders. By doing so, the potential for positive results in the medium-term increases, bringing together important players. Collaborative Decision Making (CDM) schemes constitute a class of approaches for the management of shared or public resources by a collection of private and public entities with individual goals. Transformative Technologies in Transportationxxii Transformative technologies interact with CDM among connected institutions in two inter-related respects. First, technology innovations enable new transportation services and business models that call for greater coordination among institutions in both the public and private sectors. This is necessary to achieve the potential of these technologies in serving communities in an equitable and effective manner, while ensuring the safety and integrity of these services, including data privacy and cybersecurity. Second, transformative technologies enable CDM across both existing and new institutions. There are opportunities to enhance capabilities in managing transportation systems through collaborative decision-making structures, augmented by real-time data streams and AI algorithms that facilitate information sharing and human interaction in these emerging contexts. Connectivity enables greater visibility and information sharing across jurisdictions, modes, and sectors, and can thus support collaborative approaches to decision-making and policy implementation, including better integration across modes and services, and greater responsiveness to connected travelers. Automation presents significant opportunities for safer and more energy- efficient trips, particularly through passenger mobility services. Additionally, the adoption of AI holds great potential in empowering the management of transportation systems, especially in effectively managing the supply and demand aspects. New models of public-private cooperation would also contribute to achieving the granularity and efficiency enabled by sharing economy models while mitigating the potential pitfalls of chaotic fragmentation. Electrification and decarbonization become more readily attainable when the electric grid and utilities are better connected with transportation organizations, enabling multisectoral collaboration and complementarity. While market-driven technological advancements hold the potential for significant benefits, they also entail certain risks that must be addressed. The proliferation of connected, autonomous, and shared vehicles, for instance, may disrupt larger public transportation systems and contribute to increased congestion with smaller vehicles. The advent of big data and AI further raises concerns regarding privacy and data management. To safeguard the overall well-being of society, it is imperative for the public sector to actively manage and mitigate these risks. Moreover, the potential cost of technology disruption to traditional practices requires a proactive response and innovation from the public sector. While concerns about these disruptions may occasionally be exaggerated, the rapidly evolving nature of innovation can create ongoing shocks that the public sector may struggle to cope with in many countries. By recognizing the potential challenges and actively seeking solutions, policy makers can effectively navigate the transformative impact of technology and ensure a smooth transition to a more efficient and sustainable transportation system. Question 7: What are the main technology trends in passenger mobility and how can governments enable large scale technology adoption? For a new technology to reach large-scale deployment, it typically goes through the following three stages. Firstly, technology must undergo theoretical breakthroughs, where scientists establish its feasibility in theory. Secondly, there should be a technological breakthrough, where the theoretical concepts are successfully translated into practical applications and demonstrated in real-world settings. Lastly, industrialization is essential, involving the refinement of the product or service through market forces. Provision of more of the same types of existing infrastructure would not be the most effective investment for urban futures. Rather, the urban mobility infrastructure must be re-conceived Transformative Technologies in Transportationxxiii and reinvented to better serve and enable these futures. Emerging technologies in the passenger mobility sector generally fall under four categories: connectivity, automation and autonomy, sharing mobility, and electrification. Connectivity: CVs serve three main purposes: improving safety, enhancing mobility, and reducing emissions. CV technology enables more responsive operation of traffic controls, especially traffic signals, and more efficient sharing of right of way by different types of vehicles, including transit vehicles along priority corridors. Connectivity will also enable more effective demand management by integrating information to and from travelers into the entire system and improving the overall user experience and multimodal mobility. Data and systems integration envisioned under an Internet of Things (IoT), when applied at the level of an urban area, results in smart cities, where a web of connected sensors of all types along with shared data platforms enables the realization of efficiencies across urban services in different sectors. Smart cities have implications for governance; it takes more than technology to bring about smart cities. It also takes people, communities, and institutional change, and an active program for community engagement. To evaluate the full benefits of CV technologies on the transportation system, transportation agencies must be equipped with all the necessary traffic analysis tools needed to assess potential impacts and support decision-making, both at the planning and operational levels. Automation: Vehicle automation, particularly in the form of autonomous mobility services (AMS), holds significant potential in the transportation sector. However, the limited availability of actual data on the behavior and operation of AVs and their interactions with travelers poses a challenge. It is essential for agencies at all levels to recognize that the emergence of AVs and connected, autonomous, shared, and electric (CASE) technologies is no longer a question of if, but rather a matter of when, at what pace, and in what form. This recognition should drive policy makers to take proactive measures to facilitate technology adoption in vehicle automation. Integrating AMS into existing transportation networks is a key aspect of leveraging automation. Public transportation agencies need to embrace change and reconsider their strategies to achieve their mission effectively. One approach is for transit agencies to become Mobility-as-a-Service (MaaS) providers that own AV fleets, including small buses, and offer some forms of shared AV (SAV) operations. Alternative approaches include contracting with third-party providers to deliver these services, while maintaining varying degrees of control, and letting the private sector work out preferred profitable business models, while facilitating intermodal access. The focus of transit agencies could shift toward providing high-frequency, high-capacity, and high-quality services such as rail and bus rapid transit (BRT), while relying on SAV fleets for local area travel and accessing high-capacity lines. Facilitating engagement with the private sector and application developers is crucial. Making data readily available in standard formats allows for enhanced collaboration and innovation. Additionally, considering co-branding with new service suppliers that leverage location and traffic exposure can deliver added value by creating amenities in urban spaces. By rethinking the “product” from a user experience perspective, policy makers can ensure that the benefits of automation are effectively communicated and optimized. Shared Mobility: Policy makers in developing countries have the potential to leverage a strong legacy of sharing, economic development, technological innovation, and rapid urbanization to encourage shared mobility use. They can create a supportive regulatory framework that promotes safety, fairness, and innovation in service provision. Incentives and funding opportunities can be provided to encourage shared mobility operators and support infrastructure improvements. Collaboration with stakeholders, including operators, transportation agencies, and communities, Transformative Technologies in Transportationxxiv is essential to address challenges and develop effective strategies. Policy makers should promote data sharing and integration to optimize service planning and monitoring. Public awareness campaigns can educate the public about the benefits of shared mobility. Infrastructure support, such as dedicated lanes and charging stations, can enhance accessibility. Pilot programs allow for testing and evaluation, informing expansion decisions. In doing so, developing countries have a unique opportunity to decouple economic development from increasing rates of motorization and instead, leverage sharing strategies to expand the access to and flexibility of public transportation. Electrification: Electrification offers a great opportunity for developing countries to leapfrog toward a sustainable transportation system by offering environmental sustainability through zero emissions, improved energy efficiency, integration of renewable energy sources, reduced noise pollution, economic growth and job creation, enhanced energy resilience, and improved public health. Although electric cars may still be more expensive than ICE cars, the gap in prices of vehicles is narrowing rapidly as battery costs continue to drop. A recent study1 by the World Bank found that the lower operating costs of EVs over the lifetime of the vehicles more than justify the additional capital costs in at least one third of the 20 developing countries that were studied. In general, electrification of fleet greatly improves the energy efficiency and reduces pollutions, even when a country’s power generation mix still includes significant shares for non-renewable sources. Across countries, the tendency is to tax petrol and subsidize electricity. This further enhances the energy cost advantage of electric mobility beyond what is economically justifiable. Such costs advantage, and the environmental benefits, would become stronger as countries continue to pivot towards renewable sources for power generation. National scale adoption of electric mobility is economically advantageous in many developing countries, particularly in the sectors of two- wheelers, three-wheelers, and e-buses. However, barriers such as high initial capital costs and a lack of infrastructure investment still exist. Governments need to understand both the long-term cost advantage and short-term financial challenges of implementing meaningful policy tools (for example, carbon pricing, loans for green vehicles, innovative procurement method for e-buses, and tax credits and grants) to bring the leapfrog opportunities from promises to reality. Government, overall, have a responsibility to drive the country’s economic advancement while ensuring public safety and security and adhering to sustainable and equitable practices. As such, its role in facilitating technology adoption depends on the specific technologies and the extent of readiness of both public and private sectors—in terms of infrastructure, governance, and technological capabilities. For CASE technologies in transportation, the following actions are recommended for consideration and adaptation to local conditions: • Formulate a roadmap for the adoption and deployment of the kinds of technologies and services that the government believes would be beneficial for the country. • In the telecommunications arena, adhere to international communication standards that are generally adopted by most major companies and countries. • For platform-based services, formulate a sensible policy for opening up the data market. • For data collected by agency-owned sensors, adopt cloud-based data storage and public-facing portals that enable access and valorization of those data. 1 Briceno-Garmendia, Cecilia, Wenxin Qiao, and Vivien Foster. (2023). The Economics of Electric Vehicles for Passenger Transportation. Sustainable  Infrastructure Series. Washington, DC: World Bank. doi:10.1596/978-1-4648-1948-3. Transformative Technologies in Transportationxxv • For privately obtained data (for example, cellphone data and transaction), create a legal environment that enables productive use, while protecting privacy and ensuring security. • For automation technologies, provide legal and insurance frameworks for testing, certification, and use of automated vehicles subject to safety considerations. Review infrastructure condition and needs and provide permitting structure that is consistent with the risks and benefits of the technology. • For electrification and all mobility technologies, assess infrastructure needs and consider synergistic development of multiple infrastructures (transportation, electric grid, telecoms). • Consider setting up a multiagency structure to ensure a consistent policy and integrated approach, and to avoid contradictory directives and counterproductive investment. Ensure that the technical skills are available within the requisite agencies. Question 8: How can technology improve public transit services, and what is the best approach to managing the data generated by technologies? Technology innovation plays a significant role in transforming and improving public transit services. It enables the integration of digital platforms, mobile applications, and other technological solutions to enhance the efficiency, accessibility, and safety of these services. A key role of technology innovation is providing platforms and applications that connect passengers with informal transit providers. These platforms facilitate efficient matching between passengers and drivers, enabling real-time booking, tracking, and payment functionalities. By leveraging technology, informal transit services can reach a wider audience, expand their customer base, and enhance the overall user experience. Additionally, technological innovation helps improve the safety and reliability of informal transit services. Features such as driver ratings, Global Positioning System (GPS) tracking, and emergency response systems enhance passenger safety and build trust in these services. Moreover, public transportation is essential in any MaaS strategy or business model. MaaS aims to provide integrated transportation options, and incorporating public transportation allows users to access a wide range of choices. It enhances accessibility, affordability, and sustainability, promoting shared use of transportation. By prioritizing public transportation integration, governments create inclusive mobility solutions. Technology can enable real-time monitoring and data analysis, allowing service providers to identify and address operational challenges, optimize routes, and improve service quality. Meanwhile, policy regulations play a vital role in ensuring transparency and fair competition in the transportation sector. Effective policy regulations should address data transparency, requiring transportation operators to provide open access to prices, timetables, and service information. By promoting data transparency, regulations can prevent monopolistic practices and encourage healthy competition among service providers. Furthermore, technology innovation enables the integration of informal transit services with the broader transportation ecosystem. Integration with public transit systems, ride-sharing platforms, and other modes of transportation enhances connectivity and provides passengers with seamless multimodal travel options. This integration can contribute to reducing congestion, enhancing mobility, and promoting sustainable transportation practices. Chapters 2 and 3 discuss these issues from the infrastructure and mobility services’ perspective, respectively. Transformative Technologies in Transportationxxvi The establishment of standards is instrumental in promoting the adoption, adaptation, and deployment of new technologies for urban mobility. Standards facilitate the exchange of information between institutions and enable the emergence of new players in the information ecosystem. The more mature the standards, the easier it is for new cities, regions, and countries to adopt them. This makes standardization a valuable tool for accelerating development and deployment, particularly in countries with less well-developed transportation systems. Embracing existing standards allows countries to reduce innovation costs and deployment times, fostering a more efficient and effective implementation process. General Transit Feed Specification (GTFS) and General Bikeshare Feed Specification (GBFS), explained in Chapter 5, are examples of such successful technological standards. However, standardization must be coupled with openness. Easy accessibility to standardized data is essential to build applications that can intake, process, and provide useful outputs. By avoiding the need to reinvent data filters and processing logic for each distinct organization, the application of standardized data becomes more streamlined and cost-effective. The creation of data standards requires champions, individuals, and organizations that initially promote and advocate for their adoption. This bottom-up approach, originating within the industry, ensures a more organic and effective implementation process. Vast amounts of data that are accumulating through nontraditional sources such as smartphones and internet transactions, as well as video images of the transportation system itself, are finding their way into agency practice. The prevalence and availability of such data sources varies widely across the cities of the world, reflecting different regulatory schemes that may limit the ability of the private sector to collect or provide such data, or the absence of a sufficiently large addressable market. This remains a significant opportunity for leapfrog in data collection, spatial pattern characterization, and foundational data for planning processes in many cities of the developing world. More discussions can be found in Chapter 5. The best approach to managing data involves prioritizing accessibility, privacy, collaboration, capacity building, and regulatory considerations. It is essential to promote open data policies, ensuring easy access to relevant transportation data while implementing robust privacy and security measures to protect sensitive information. Investing in data collection enables the capture of valuable insights for decision-making and service improvement. Collaborations among public institutions, private companies, and informal transit providers is critical for data sharing and coordination. Capacity-building efforts should focus on training stakeholders in data management practices, while public awareness and engagement activities help gather feedback and address community needs. Clear regulations and guidelines must be established to govern data management, ownership, and usage. By adopting this comprehensive approach, developing countries can effectively harness data to enhance their informal transit services. Question 9: How can innovation in digital infrastructure maximize the potential from multimodal integration platforms? Technology-driven multimodal integration holds immense promise for transportation systems across countries. The role of digital infrastructure, alongside physical infrastructure, is paramount in maximizing the benefits of integration. Chapter 2 points out that governments and policy makers should value and prioritize the development and integration of digital infrastructure to unlock the full potential of existing physical infrastructure systems, fostering efficient, connected, Transformative Technologies in Transportationxxvii and sustainable transportation networks that cater to the evolving needs of societies and economies. In addition, it should be noted that the efficient utilization of digital infrastructure, encompassing both connectivity and digital platforms, is crucial for maximizing the benefits of multimodal integration in transportation systems. The synergy among different sectors, such as transportation, telecommunications, and data analytics, plays a pivotal role in realizing the full potential of integration. By explicitly highlighting the importance of efficient digital infrastructure utilization and fostering collaboration between sectors, governments and policy makers can unlock new opportunities for seamless integration and enhance the overall performance of transportation networks. Multimodal integration in transportation systems has gained significant advantages through the widespread adoption of technology across countries. Governments and policy makers should recognize the importance of digital infrastructure as an essential component along with physical infrastructure. While the costs associated with equipping existing infrastructure with ICTs are relatively marginal compared with the construction of new physical assets, the potential benefits are substantial and far-reaching. A World Bank study2 provided an exemplary illustration witnessed in the implementation of smart port systems that showcased remarkable efficiency improvements by drastically reducing cargo and vehicle handling time from an average of 15 hours to a mere 2.5 hours. Additionally, they have significantly streamlined administrative processes by reducing the number of documents submitted by an agent from an overwhelming 53 to a streamlined 11. Such impressive advancements underscore the immense potential of integrating ICT solutions into existing physical infrastructure to enhance overall system performance. Connectivity and the IoT play pivotal roles in unlocking the benefits of multimodal integration. The more “things” that are connected, the more sectors integrated within a city, the greater the potential for optimizing multimodal transportation networks. It is worth acknowledging that achieving seamless integration and realizing the vision of smarter urban systems may present challenges, particularly from the public sector. The public sector often holds vast amounts of sensor data and operates critical infrastructures and services, requiring extensive coordination efforts and process redesign to fully leverage the advantages of urban-scale connectivity. The level of adoption and the models of public-private engagement may differ across cities due to varying capacities, contextual factors, and governance structures. Furthermore, it is crucial to understand that developing countries are not constrained by their existing legacy transportation systems. Conventional transportation infrastructures can evolve into smart infrastructure when equipped with essential components such as sensing capabilities, computing power, and implementation capacity. Using a sensor-controller-actuator model, transportation agencies can evaluate the untapped potential of their existing infrastructure and identify new opportunities for integration and enhancement. Question 10: What are the main technology investment areas in transportation at the World Bank and how to scale up the engagements in transformative technologies? To bring about the potentially transformative role of the discussed technologies in advancing the environmental sustainability agenda, about 400 World Bank transportation projects initiated 2  he World Bank, 2020. “Accelerating Digitization: Critical Actions to Strengthen the Resilience of the Maritime Supply Chain.” World Bank, T Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO Transformative Technologies in Transportationxxviii within the past 15 years were reviewed. These projects cover most of the geographic regions in the developing world and all five transportation modes (road, public transit, railway, maritime, and aviation). Most of them focus on transportation infrastructure improvements. However, a non-negligible part is dedicated to “soft” components such as digital platforms, policy framework, and institutional capacity enhancement. The findings are summarized based on four technology categories: ICTs, ITS, digitalization and asset management, and e-mobility. For more details on the projects and regions these technologies are applied to, see Annex 5. Information and Communication Technologies ICTs are common components of road transportation projects. Many of these ICT components are related to the deployment of an optical fiber network along the road project, which serves not only the needs of the transportation system (transferring data), but also other information needs along the corridor. In many parts of the developing countries, essential ICT equipment such as fiber optic networks are not available. Therefore, such investments have been considered a prerequisite for the deployment of more advanced ITS applications or smart mobility solutions. However, wireless communications, especially 5G and upcoming 6G networks, are increasingly capable of delivering the bandwidth capacity previously delivered through fiber lines. Funding for ICT infrastructure projects is important. This includes investments in broadband networks, mobile connectivity, and data centers, which form the backbone of ICT ecosystems. Equally important is to support capacity building and technical assistance programs. These initiatives can help developing countries build the necessary skills and knowledge to effectively utilize and manage ICTs. Capacity building efforts can include training programs, knowledge exchange platforms, and policy advisory services, which enable countries to leverage ICTs for sustainable development and economic growth. The World Bank has been supporting developing countries’ leapfrog opportunities in ICT development. It provides financial resources, technical expertise, and policy guidance to help countries design and implement effective ICT strategies. Additionally, knowledge sharing and best practices have been facilitated and promoted in ICT development through its global networks and partnerships. It supports countries in creating enabling policy environments, developing regulatory frameworks, and implementing effective governance structures for ICTs. For example, the Eastern Africa Regional Transport, Trade and Development Facilitation Project, which aims at improving the movement of goods and people along Lokichar-Nadapal/Nakodok in Kenya, includes a $29 million investment in optic fiber network along the corridor and at the Kilindini port, out of a total investment of $500 million. The objective is not only to enhance the internet connectivity and road management, but also to allow the residents living along the project corridor to have greater access to economic opportunities and essential services. Another example is the Western Economic Corridor and Regional Enhancement Program in Bangladesh, which aims to provide efficient, safe, and resilient connectivity along a section of a regional transportation corridor and reducing postharvest losses in the hinterland. An investment of $2 million is dedicated to the development of an optic fiber network and the deployment of ITS along the Jashore-Jhenaidah national highway. Intelligent Transportation Systems Conventional ITS such as traffic management centers, or advanced traffic management system (ATMS), are still a popular component of many transportation networks in developing countries. However, many developing countries lack not only the ITS equipment, but also the experience and skillsets to operate the system. Therefore, such ITS investment usually includes a component dedicated to capacity building. Transformative Technologies in Transportationxxix Funding ITS projects include investments in ATMS, smart signaling, integration of vehicles with infrastructure, and data collection and analysis tools. Financial support from these allows countries to upgrade their transportation infrastructure and implement ITS solutions that improve safety, efficiency, and sustainability. In addition, they contribute to capacity building and knowledge transfer initiatives. Developing countries often require technical assistance, training programs, and expertise to effectively implement and manage ITS projects. By allocating funds for capacity-building activities, it can enable countries to acquire the necessary skills and knowledge in areas such as data analysis, system integration, policy development, and institutional capacity strengthening. The World Bank has been providing financial support to help countries develop comprehensive ITS strategies and implementation plans. For example, the São Paulo Aricanduva BRT Corridor dedicated $12 million toward upgrading the bus operational control center, capacity building, and training required to run the operational control center (OCC). The Ulaanbaatar Sustainable Urban Transport Project includes the upgrading of the Area Traffic Control system and equipment, and on-street ITS equipment such as traffic signals and monitoring cameras. It also includes a smart parking system and institutional capacity building to effectively manage data-driven planning and emerging mobility services such as MaaS. Furthermore, the World Bank can support the dissemination of successful ITS case studies, lessons learned, and policy recommendations, allowing countries to learn from each other’s experiences and avoid potential pitfalls in ITS deployment. Digitalization and Asset Management Digitalization and the development of transportation asset management systems are important components of many transportation infrastructure projects. The Second Rural Transport Improvement Project in Bangladesh aims at developing comprehensive IT-supported road asset management policies, systems, and operations including the piloting of alternative pavement design and building with maintenance technologies. The Vietnam Road Asset Management Project also aims at improving the efficiency and sustainability of the road asset management and maintenance practices of the Ministry of Transport on national roads. Developing or improving an asset management system is included in more than a third of the transportation infrastructure projects that were reviewed in this study. This high percentage is related to the fact that the level of digitization of transportation assets is still low in developing countries, which prevents effective planning and management. Digital development is an important enabler for other technology innovations in the transportation sector. Funding support in digitalization projects can enable developing countries to invest in digital infrastructure, software systems, and data management tools. Technical assistance and expertise to support the digitalization process are equally important. This can involve providing guidance on best practices, conducting assessments and evaluations, and offering specialized knowledge in digital asset management. Developing countries show great leapfrogging potentials with the benefit of digital development. Starting in 2016, Kenya gradually replaced the red booklet driving license with a smart chip- embedded card based on the newly developed transportation integrated management system.3 This innovation reduces the wait for applying for a driver’s license from six months to a few days. The smart driver’s license can easily be verified on the spot by traffic enforcement officers through 3  asia, Josphat (2021), Smart Driving License revolutionizes management and security of Kenya’s transport sector, October 19, 2021 https://blogs. S worldbank.org/nasikiliza/smart-driving-license-revolutionizes-management-and-security-kenyas-transport-sector?deliveryName=DM120328 Transformative Technologies in Transportationxxx a mobile application, which contributes to better management and added security of Kenya’s transportation system. The smart system also increases the reliability of the driver’s license service through virtual connections, which was particularly useful during the COVID-19 pandemic. The World Bank, through its expertise and global reach, can effectively support developing countries in capitalizing on leapfrog opportunities for digitalization and asset management, ultimately driving sustainable development and improved asset performance. E-mobility Electric mobility is gaining significant momentum and growing interests are often motivated by decarbonizing the transportation sector, but the rationale for the transition is much wider for LMICs. It has the potential to reduce local air pollution, improve the quality of public transportation, provide last-mile connectivity, reduce dependency on imported fuels, and provide opportunities to participate in vehicle supply chains. E-mobility must be part of a comprehensive program to promote sustainable and inclusive urban mobility. A World Bank global study4 finds that in half of the 20 developing countries studied, EV transition is already making economic cases; in particular, electric buses and two- and three-wheeled vehicles are effective entry points and bring environmental benefits even before the power grid is fully decarbonized. Another recent World Bank study provides a comprehensive analysis of the case of the Shenzhen Bus Group Company in the City of Shenzhen, China, which electrifies its entire bus fleet in a relatively short time period.5 The study showed that electrification of public transportation in Shenzhen significantly reduced GHG emissions and air pollution, though the exact environmental benefits depend on the source of electric power generation but are sizable in all cases. The wide adoption of EVs in developing countries is facing several challenges and financing mechanisms can play a crucial role in overcoming those barriers. The upfront cost of EVs remains higher than the traditional ICE alternative, acting as a barrier, particularly in lower-income populations and countries. As EV technologies evolve and costs go down, wider adoption might be economically viable when EVs’ lifetime advantages are considered, and innovative financing structures can be made available to overcome cost barriers. By offering financing support to the installation of charging stations and associated grid upgrades, it will enable developing countries to overcome the initial high costs of establishing a robust charging network. This also helps alleviate concerns related to the availability and accessibility of charging infrastructure, encouraging EV adoption. Moreover, it is important to make investments in power grid upgrades to accommodate the increased load from EV charging and ensure the reliable and sustainable operation of other infrastructures and buildings. This can involve strengthening distribution networks, implementing smart grid technologies, and integrating renewable energy sources to enhance the resilience and capacity of the power infrastructure. The World Bank is having active dialogues on e-mobility in many developing countries, through lending and advisory programs at various levels of engagements, to influence a speedy transition to e-mobility as an enabler to decarbonize the sector. The World Bank is also developing innovative mechanisms that will make EVs more affordable and help mobilize more financing for electric mobility. In Africa, the Bank supports several countries to evaluate the feasibility of introducing 4  riceno-Garmendia, Cecilia, Wenxin Qiao, and Vivien Foster. (2023). The Economics of Electric Vehicles for Passenger Transportation. Sustainable B Infrastructure Series. Washington, DC: World Bank. doi:10.1596/978-1-4648-1948-3. 5  World Bank. (2021). Electrification of Public Transport: A Case Study of the Shenzhen Bus Group. Mobility and Transport Connectivity. World Bank, Washington, DC. © World Bank. Transformative Technologies in Transportationxxxi e-buses in BRT projects and design pilot programs. In Dakar, the World Bank is financing the very first all-electric BRT bus fleet to operate in Africa. In Chile and Egypt, the Bank is supporting the procurement of e-buses. In India, the Bank is working closely with the government to set up a risk- sharing facility that will help mobilize more financing for two- and three-wheelers and bring down borrowing costs, as well as supporting an ambitious bulk procurement and de-risking program for electric buses with the objective of reducing acquisition costs and deploying up to 50,000 e-buses across the country. In Brazil, the Bank is financing e-buses through two projects in São Paulo and Santa Catarina state and had facilitated knowledge sharing in five Latin America and the Caribbean (LAC) cities to bring the government counterparts up to speed on the enabling factors and roles of stakeholders in the e-mobility agenda. How Can We Scale up the Engagements in Technology Innovation? Recognize and leverage broader social benefits. In many cases, technology-related transportation investment generates additional social benefits beyond the original objectives of the projects. Highlighting the positive impact of technology on areas such as environmental sustainability, social equity, and economic development will help showcase the broader value of investing in transformative technologies and win wider support from the public sector. To scale up the engagement in technology innovation, there is a need to leverage the lessons learnt, promote inclusive development, and support developing countries in the process of adopting transformative technologies for their transportation systems. Enhancing transportation system resilience through technology adoption. Lessons learned from projects like the smart driver’s license system in Kenya demonstrate how technology can increase the reliability and responsiveness of transportation services during crises such as the COVID-19 pandemic. Emphasizing the integration of climate resilience measures into infrastructure projects can assist climate-vulnerable countries to build robust and adaptable transportation systems. The digitization of transportation assets helps countries better prepare themselves for natural disasters and emergency responses. These examples present the value of enhancing the resilience of the transportation system through technology innovation, which is particularly important given the context of climate change and the experience gained from the pandemic. Empowering women through technology deployment. Leveraging technology adoption can enhance the safety and economic opportunities for women through transportation. Building on projects like the Operational Control Center upgrade in São Paulo and the Cebu BRT Project in the Philippines, the Bank continues to support initiatives that provide safer and more inclusive transportation options for women. By integrating technologies that address concerns like sexual harassment, the Bank can contribute to creating environments where women feel secure and empowered to access transportation services. Furthermore, the deployment of ICT systems can expand economic opportunities for women by improving their access to the internet, digital services, and entrepreneurial platforms. Valuing data as a strategic asset. Recognizing the significance of data as an asset associated with technology investments is crucial. Based on the findings of the World Bank study6 on automated fare collection systems in African cities, it is evident that these systems serve as rich data sources that can be used to improve service quality, optimize transportation networks, and enhance overall 6  rroyo-Arroyo, Fatima, Philip van Ryneveld, Brendan Finn, Chantal Greenwood, Justin Coetzee (2021), Innovation in Fare Collection Systems for Public A Transport in African Cities, SSATP Technical Note, June 29, 2021 Transformative Technologies in Transportationxxxii system performance. Actively promoting the utilization of data-driven approaches and analytics to inform decision-making processes can support efficient planning and enable evidence-based interventions. By facilitating the collection, analysis, and utilization of relevant data, as well as business opportunities for the private sector, the Bank can help countries maximize the benefits of technology in their transportation systems. Strengthen policy and regulatory frameworks. To support the effective deployment of transformative technologies in transportation, it is crucial to develop robust policy and regulatory frameworks. These frameworks should address issues such as data privacy and security, interoperability standards, fair competition, and equitable access to technology-enabled services. By working closely with governments and policy makers, the Bank can help ensure that the necessary legal and regulatory conditions are in place to foster innovation, protect user rights, and promote sustainable and inclusive technology adoption. Foster knowledge sharing and capacity building to prepare for technology readiness for deployment. Developing countries reveal a growing investment in transportation technologies as part of infrastructure development. However, the overall scale of investment and the complexity of technology implementation are still lagging. To adopt and adapt technologies to serve mobility needs efficiently and sustainably, stakeholders need technology readiness, the availability of supportive infrastructure, institutional capacity, and the presence of a skilled workforce. Addressing these challenges requires focused efforts to enhance technology readiness through research, innovation, and development initiatives tailored to the specific context of developing countries. Additionally, investments in critical supporting infrastructure, such as communication networks and data management systems, are essential for effective implementation. Developing institutional capability and capacity-building programs can further support the successful integration of these technologies into transportation systems. This includes fostering partnerships between public and private sectors, facilitating knowledge transfer, and providing training opportunities to develop a skilled workforce capable of managing and leveraging these technologies. By leveraging knowledge expertise and global best practices, with well-designed financing mechanisms, the World Bank can help developing countries overcome technical and operational challenges associated with technology adoption. Overview of Transformative Technologies in Transportation 1 Transformative technologies revolutionize transportation, offering higher mobility at lower economic and environmental costs Adopting such transformative technologies in the transportation sector could help countries, particularly developing ones, to achieve economy growth and human development goals in a sustainable way. Tech Innovations in the Transport Sector Ubiquitous and High-speed Connectivity Digital Platforms with Data Analytics and AI 5G and satellite-based internet access Real-time info and logistics optimization Automation Alternative Energy for Decarbonization Autonomous Vehicles and drones Electric vehicles and sustainable fuels Institutions Infrastructure Digital Platforms: Real-time traffic prediction and Vehicles Smart Roads: Tech-enabled management roads for connected vehicles Connected Automated Vehicles: Demand and Supply Users Advanced functionalities Internet of Things Devices: Management: Data-driven and safety Enhanced driving safety and traffic control and monitoring Advanced Traveler efficiency Information System: Advance Vehicle Control Systems: Public Transport Collision warning, automated Physical and Software Enhancements: Improving Real-time travel info for features Infrastructure: Integrated accessibility and safety better decisions energy and road monitoring Digital Transformation: Platooning and Efficiency: Increased Self-services, door-to-door capacity with reduced headways experience Drones for Freight Delivery: Efficient Freight Transportation and advanced delivery mode Innovations: End-to-end monitoring, on-demand warehouses The four elements of the transport network Benefits of Transformative Technologies Economic Growth Boosting trade and production processes Environmental Sustainability Decarbonizing transport for a greener future Enhanced User Experience Improved services and efficiency for travelers Infrastructure Optimization Smart roads and IoT devices for safer journeys Transformative Technologies in Transportation3 Chapter at a Glance Technologies become transformative when they change the paradigm, enabling higher mobility levels that can be attained at significantly lower economic and environmental costs. In the past decade, driven by a set of emerging transformative technologies, transportation has been quickly evolving, adapting, and shaping global megatrends, promoting mobility, energy efficiency, and environmental quality. These technology advancements roughly fall into four categories: connectivity, digital platforms, automation, and alternative energy sources. They are transforming the transportation sector by changing or interacting with the four main elements in the transportation network, which are the users, vehicles, infrastructure (physical and digital), and institutions. This chapter sets the stage for discussion around the application of transformative technologies by briefly illustrating the technologies to be discussed here and how they may affect the four interactive elements of the network. This chapter also presents the guiding questions to understand how transformative technologies can influence transportation service delivery in developing countries, unlock opportunities for development through enabling transportation systems, and structure policy responses and interventions by governments and other stakeholders to realize positive dividends from innovation in the sector. Transformative Technologies in Transportation4 Context Sustainable and smart mobility is a fundamental element to achieve poverty reduction and shared prosperity. Historically, major expansions of transportation networks have been associated with economic growth and social changes. The rapid expansion of the rail network in Britain in the 1800s became a powerful enabling force to the industrial revolution. It allowed agricultural products of remote villages to access major markets in the cities and raised the standard of living (Wilde, 2019). Today, improving transportation networks and services has become even more critical for development as it provides key access to markets and talents. However, the improvement is no longer just focused on the expansion of road capacity, but also focuses on using the existing system more efficiently, as other issues related to the transportation sector are becoming increasingly challenging, including traffic congestion, local air pollution, energy consumption, GHG emissions, safety, and security. Transportation is quickly evolving, adapting, and shaping global megatrends, notably promoting energy efficiency and environmental quality. Strategic goals for many countries also include providing safe and efficient travel choices, improving mobility for all, and enhancing the resilience of mobility and supply chains. An efficient and sustainable transportation network is required to reduce urban congestion, air pollution, and traffic accidents, and provide key access to economic opportunities. The network resilience is imperative for green economic recovery, the importance of which has been illustrated by numerous natural or man-made disasters in recent years. One of the fundamental enablers to achieve these goals is found in ITS, which apply sensing, location, information, and communication technologies to the surface transportation system. This process was initiated more than two decades ago to leverage the power of rapid technological developments, allowing users and system operators to be better informed and make safer, coordinated, and smarter use of transportation networks. As the underlying enabling technologies continue to evolve and new ones emerge, so has the range of applications in the transportation domain. Today, the key technological innovations expected to shape and transform the future of transportation fall into one or more of the following categories: connectivity, digital platforms, automation, and alternative energy. For example, MaaS makes on-demand multimodal choices for individual travel available at the fingertip (or voice) of a smartphone user. New mobility services propelled by technological innovation are user-centric and demand-responsive, and thus smarter than the legacy system. Although still in the testing mode, AVs have moved from laboratories to the city street. The number of vehicles powered by electricity from renewable energy sources is growing rapidly. Digital transformation and drone technology are creating new possibilities for the doorstep delivery of goods purchased online. Digitalization and IoT technologies are making ports smarter and more resilient to disruptions and economic shocks. While these technological and management innovations are promising, many issues remain to be addressed. Technologies become transformative when they change the paradigm, allowing us to do new and different things that were not previously possible. New technologies emerge all the time. Companies continuously improve their offerings; third parties continually come up with new ideas and features for existing products. Throughout history, and especially in the past two decades, there has been a progressive adoption of ICT in all facets of business and everyday life, including transportation systems. The first order impact of these technologies is to allow the same processes to be performed better, faster, and cheaper. However, the processes and the nature of the service themselves remain essentially the same, until such incremental changes lead to a paradigm shift. Transformative Technologies in Transportation5 When a physical map is replaced with a digital one but is used in the same way as a physical map, the impact is incremental in terms of convenience and interactivity. When we overlay real-time predictive travel time information, the technology becomes transformative because it enables real-time routing and may change the entire process of planning and executing travel. Transportation technologies are transformative when they enable higher levels of mobility for users, at lower environmental and carbon impact, while creating new opportunities for service delivery options that would not have been possible or practical (operationally or economically) in the past. In other words, transformative technology enables enhanced mobility levels that can be attained at significantly lower economic and environmental cost. The wide deployment of freeways and the high-speed rail system starting from the 1950s fundamentally changed where people lived and worked, and consequently reshaped the landscape of cities, large and small. Benefiting from the enhanced mobility, people had better access to jobs and other economic opportunities concentrated at city centers and affordable housing and ease of life at the suburban areas. The growth of freeway and high-speed rail systems greatly expanded the concept of cities and created mega-cities and metropolitan areas. The economy of scale and scope enabled by these fast and convenient transportation networks brought new vigor to these metropolitan areas and further fostered economic growth. As a recent example, the rapid growth of the Chinese economy since the 1980s has been accompanied by an unprecedented investment in the freeway network since 1990s and the high-speed rail network in 2000s. These interconnected cycles between economic growth and transportation network expansions created several mega metropolitan areas in China, including the ones at the Yangtze Delta and Pearl River Delta, all of which served as major economic hubs that collectively propelled the extraordinary economic growth in China. The high density in city centers, enabled by the expanding transportation network, creates economy of scale and scope, but it also creates negative externalities such as traffic congestion, air pollutions, and GHG emissions. In the U.S., the 2019 Urban Mobility Report developed by the Texas A&M Transportation Institute (Schrank, Eisele, and Lomax, 2019) shows that from 2012 to 2017, congestion cost per auto commuter increased by 11 percent and “wasted” fuel per auto commuter increased by 5 percent. As the urbanization process continues, particularly in the developing countries, these problems become more acute. The 2019, the TomTom Traffic Index based on location data collected by navigation devices, in-dash systems, and smartphones showed that traffic congestion worsened in 239 out of the 416 largest cities across 57 countries on six continents. The top five cities on this list, Bengaluru in India, Manila in Philippines, Bogota in Columbia, Mumbai in India, and Pune in India, are all from developing counties and saw rapid urbanization. In the worst case, travelers in Bengaluru need to spend 71 percent more time in congested traffic than what people would have done under uncongested conditions. As countries seek further economic growth and the demand for mobility and better accessibility to markets and talents keeps growing, transportation professionals worldwide look for smarter and more sustainable transportation solutions. Over the last 10 years, the world has started to see the convergence of a few transformative technological innovations that are shaping and reshaping the landscape of the transportation network. New transportation services and business models are created, tested, and improved, through which transportation professionals and policy makers start to see exciting megatrends for future transportation systems. These new transportation services are usually user-centric and demand-responsive, and the transportation system promises to be safer, greener, and more efficient. Riding on these megatrends and quickly and effectively adopting the transformative technologies in the transportation sector could help countries, particularly developing ones, to achieve economy growth and human development goals in a sustainable way. Transformative Technologies in Transportation6 However, the pathway to smart and sustainable mobility is not straightforward. The transportation sector has been historically subject to government regulations because many services carry the nature of natural monopoly and because of its various types of externalities, from the negative impact on air pollution and congestion, to the network externalities present in platforms, passing through moral hazard linked to transportation safety. Deploying transformative technologies in the transportation sector today is even more challenging, particularly in urban areas. This is because transportation is such an essential service that it has permeated many other areas, including safety, privacy, data security, land use, the labor market, and competition and integration with incumbents and legacy systems. Failing to consider these factors, on top of controlling other well-known externalities, may lead to failure instead of the promised benefits. Past experience shows that technologies that were successfully adopted and adapted were those that took advantage of existing infrastructure and technologies. They enjoy economy of scale through rapid production and deployment to reduce costs and make a profit. One prerequisite for the technology to reach large-scale deployment is that it should have gone through the three steps of preparations: 1. Theoretical breakthrough where scientists have proved its feasibility in theory. 2. Technology breakthrough where the theory has been successfully translated into application and demonstrated in practice. 3. Industrialization, in which the product or service is refined through market forces. The transportation sector is prone to market failures, and technological innovations are not immune to this. Some of these emerging transformative technologies had been controversial during their initial deployment. Erhardt et al. (2019) showed that transportation network companies (TNCs) such as Uber and Lyft are the biggest contributors to the growing traffic congestion in San Francisco. Qian et al., (2020), drew similar conclusions using data from New York City. As the urbanization process continues, particularly in developing countries, according to the prediction of World Urbanization Prospects by the Population Division of the UN Department of Economic and Social Affairs (UN DESA), neglecting the potential negative impact could threaten the objective of developing a sustainable transportation system. Technological innovations may also have a disruptive impact on the labor market. For instance, TNCs have negatively affected the incomes of taxi drivers. Yet, as AV technologies rapidly progress, a larger threat looms over not only taxi drivers, but also bus and truck drivers. However, potential negative impacts can be mitigated and should not deter the pursuit of positive dividends. TNCs could be integrated with public transportation services to expand the scope of MaaS and create a win-win situation. Labor replaced by AVs could be retrained to fill new jobs created by the new economy. Historically, many transportation services exhibit features of natural monopolies such as reduced competition due to high initial costs and other barriers to entry as well as significant economies of scale. Moreover, the transportation sector is characterized by regulations targeting the associated externalities. In the wake of emerging technological innovations, how we manage technological shifts will have profound consequences for the impact on decarbonization of the sector, reduce congestion and increase access to social and economic opportunities. With the rapidly evolving landscape of the modern transportation network, the knowledge gap in how to leverage opportunities, seize benefits, fence problems, and discover the potential role of transformative technologies in addressing key development challenges is becoming increasingly evident. For transportation and development practitioners, opportunities for transforming the Transformative Technologies in Transportation7 sector through adoption and deployment of transportation technologies are enormous, though they may entail a structural change in existing business models. Modern transportation systems are being structurally challenged by new business models, and new types of transportation services flourish because of technological developments, the data and digital revolution, and the social behaviors and trends linked to those. Accordingly, the role of government and the regulatory tools also need to adapt so technologies can be efficiently and effectively deployed, and act as a catalyzer for shared prosperity. This structural change is so critical that the U.S. Department of Transportation (US DoT) created a new Non-traditional and Emerging Transportation Technologies (NETT) Council to ensure that the traditional regulatory structure does not impede the deployment of new technology (US DoT, 2020). The stakes of taking the “right” actions to leapfrog in the transportation sector and in human development are high for developing countries seeking opportunities of technology adoption. References on these important issues that can inform decisionmakers and guide the dialogue on policies are urgently needed. The empirical evidence documenting the impact of institutions and policies in support of technology adoption is mostly linked to developed countries. The still-emerging nature of several of the relevant technologies does not allow for a robust long-term experience to build on. It is imperative to revisit the documented lessons from the developed world and a few special cases from China, India, and so on, to adapt and understand them to best serve the development challenges. This report intends to understand how transformative technologies can influence the developing countries’ transportation service delivery, unlock opportunities for development through enabling transportation systems, and structure policy responses and interventions by governments and other stakeholders to realize positive dividends from innovation in the sector. Specifically, this report aims to fill the knowledge gaps related to the transformative role of technologies in the transportation sector of developing economies. This report does not aim to provide a detailed and exhaustive listing of transformative technologies that are under development and/or deployment, as it is a dynamic area that tends to be covered in a huge body of literature focused on the technical aspects. The report examines critical transformative technologies through the lens of the four essential elements in the transportation network: users, vehicles, physical and digital infrastructures, and institutions. This approach recognizes that each of these elements, while individually indispensable for shaping the landscape of the transportation network, is affected by and deals with technology in an unequivocal manner. The report will strategically select the transformative technologies to be analyzed, prioritizing those that have demonstrated or are considered likely to affect salient development challenges such as decarbonization, safety, planning, affordability, accessibility, and resilience, and explicitly address the following questions: • What are the principal transformative technologies on the horizon, and over what time frames is their deployment at scale expected to have an impact on mobility in different parts of the world? What is the current landscape for the development and deployment of these technologies? • Where have these transformative technologies shown their largest impact, and what are the types of technologies that have been available? In some instances, the technologies per se may be in various development and deployment stages, though some of the business models expected to operate them may be in full-scale deployment (for example, ride-hailing apps as precursors of autonomous fleet mobility services). The report will characterize, based on existing experiences, the spatial, social, economic, and institutional contexts that have led to desirable as well as unintended results. Transformative Technologies in Transportation8 • Why do transformative technologies in the transportation sector matter to development? The report will explore the social and economic benefits that can result from the adoption of transformative technologies and identify the ways they can modify the trajectory for pursuing key development challenges such as poverty, inequality, and carbon neutrality. Keeping in mind that technology may be a double-edged sword, the report will also discuss potential pitfalls and unintended consequences that might need to be averted through informed public policy. • How can institutions and policies make the adoption of transformative technologies in the transportation sector viable and beneficial for countries from a societal perspective? The report will provide guidance regarding policy directions, interventions, and evolution pathways that could unlock, for developing countries, the benefits and possibilities of leapfrogging their mobility systems (for goods and people). This includes defining the role of government agencies and intergovernmental organizations, and international stakeholders, as well as the interactions with private-sector stakeholders in industry and business communities. By answering the above questions, this report will examine the transformative technologies, explicitly addressing: • The engineering perspective, by structuring existing knowledge and international experiences in transformative technologies. • The economic perspective, by examining the economic, social, and environmental impacts of these technologies under a development context. • The policy perspective, by providing information to decision makers on how to better plan, operate, manage, and regulate transformative technologies. This report will examine the transformative technologies organized along four main categories— connectivity, digital platforms, automation, and alternative energy sources—and see how these technologies are transforming the transportation sector by changing or interacting with the four main elements in the transportation network, which are: • The users (travelers, goods) • The transporting vehicles or conveyances (cars, ships, planes, drones, micromobility tools) • The infrastructure (roads, waterways, airline facilities, digital infrastructure) • The operating institutions (governments, agencies, organizations, private companies) This will help investigate how technologies and innovations could serve each entity better and identify the key factors for successful deployment. By doing so, this report will highlight the role of public sectors in this process and what countries (particularly developing ones) could do to ride on the megatrends and achieve their development goals through the successful deployment of transformative technologies. This transition in transportation technologies offers leapfrog opportunities for developing countries toward developing a sustainable transportation system. Some enlightening successes do emerge in developing countries. Technology adoption and deployment is not a one-size-fits-all proposition; different countries may follow different adoption and adaption paths. Local conditions in certain developing countries may lead to novel adoption processes, and may even foster new technologies or business models that lead the world. As the main provider of the transportation infrastructure, the public sector has an important role to play, and institutional capability is a critical factor for Transformative Technologies in Transportation9 success. The review of recent technological trends in the transportation sector presented in this report and discussions about opportunities and barriers for technology adoption and adaption could inform decisionmakers in the public sector and help enhance related institutional capability. The Four Entities in the Transportation Network Users (Travelers/Goods) Advanced traveler information system (ATIS) is user-centric and provides real-time travel information to travelers either in their vehicles, at their homes, or at their places of work. Communicated information may include travel times, location of incidents, weather conditions, road conditions, optimal routings, lane restrictions, timetables, tracking vehicle movement from safety and security point of view, and so on, all of which can help travelers make better and informed decisions regarding routes, mode choice, departure time, and so forth. Innovation advances in communication technologies and digital platforms will transcend the boundary of conventional transportation modes and realize door-to-door services. No longer limited to trip planning and booking, digital transformation is accelerating the implementation of self-services at terminals, such as self-check-in, self–bag drop, and biometrics (ALG, 2020). Transformative technologies also have strong influences on users’ and service providers’ behavior, particularly regarding trade-offs in the use of space and resources. The transportation system carries not only passengers but also commodities and goods. Mirroring their passenger counterparts, freight transportation systems are also the target of innovations through digitalization and automation. The wide deployment of IoT sensors and rapid digitization in freight-forwarding accelerates the development of end-to-end supply-chain monitoring platforms, on-demand warehouses, and freight-matching platforms. These transformations affect not only how freight is transported, but also to a large extent, how production is organized and trade is conducted, thus significantly impacting the broad economy. Vehicles (Cars/Ships/Trains/Planes/Drones) One of the leading technologies under this category is connected and autonomous vehicles (CAVs). The autonomous (driverless) cars feature an array of sensors to detect other vehicles and obstacles; it requires detailed maps and uses machine learning to make software smarter. Connected vehicles (CVs) can communicate with each other, roadside devices (traffic signals), or non-motorized users (smartphones and other advanced devices) with cascade phasing from vehicle to vehicle (V2V), vehicle to infrastructure (V2I), to vehicle to anything (V2X). A combination of the two, CAVs leverage both autonomous and connected vehicle capabilities. From the system perspective, CAV technologies will further revolutionize advanced vehicle control systems (AVCSs). AVCS include a broad range of concepts that will become operational on different time scales. Existing collision warning systems would alert the driver on an imminent collision. In more advanced systems, the vehicle would automatically brake or steer away from a collision. The automated highway system (AHS) provides special lanes where all vehicles would be automatically controlled and coordinated to avoid issues related to mixed traffic in the early stage of CAV deployment. Cars and trucks can run in platoons and headways could be greatly reduced to increase capacity efficiency. Drone is another remarkable emerging mode for freight delivery. These ITS platforms that either have been developed or are under development provide an ideal interface for the new vehicle technologies. Transformative Technologies in Transportation10 Infrastructures (Roads, Waterways, Airline Facilities, Railway, and Communication Networks) The proposed smart road concept envisions innovative technologies that are incorporated into roads to facilitate the operation of CAVs, for advanced traffic lights and street lighting, and for monitoring the road condition, traffic volume, and vehicle speeds. Smart roads use IoT devices to make driving safer, more efficient, and more sustainable. Smart roads would combine the physical infrastructures (sensors and solar panels) with software infrastructure (AI and data) to generate energy, communicate with AVs, monitor road conditions, and more. In the freight industry, automation in the freight-handling process (warehousing, distribution center, and so on) is also rapidly evolving, driven by transformative technologies. Digitalization in the industry, from transactions, to real-time visibility, to contactless interactions, is particularly spreading during the pandemic. Institutions (governments, agencies, organizations, companies, communities) The institutions play a critical role throughout the process of planning, operations, and management of the entire transportation network. With the emerging new technologies, a large portion of the government agencies’ function is realized through digital platforms. For example, the advanced traffic management system (ATMS) is a core platform in ITS that can predict traffic conditions and improve the efficiency of the transportation network by managing traffic both from the demand side and supply side. Real-time data are collected, used, and disseminated and can further alert operators. From the demand management angle, the ATMS can monitor the network with the data collected from various sensors and predict traffic volume so that the corresponding traffic control can be advised. From the supply side, smart parking management is an example that shows facility availability, dynamic rates, and accessibility. Electronic tolling requires technology support for vehicle identification using automatic vehicle identification (AVI), payment method, and so on. Incident detection will also be a critical function in road safety improvement. The advanced public transportation system (APTS) adopts advanced technologies to greatly enhance the accessibility of information to users of public transportation as well as to improve the scheduling of public transportation vehicles and the utilization of bus fleets. The advanced rural transportation system (ARTS) has a larger implication that would provide safety and security crash warning system, hotspot identification, reduction of the severity of incident through improved response time and post- crash care, and advice on maintenance resource allocation. All these ITS platforms will benefit from digital transformation, and their deployment will be accelerated through established management systems and information distribution channels. Meanwhile, transformative technologies are also changing the landscape of transportation and mobility companies. Technology companies (Google, Microsoft, Apple, and Amazon) are taking a keen interest and playing an increasing role in the transportation and mobility sector while traditional transportation companies (for example, Ford, Boeing) are also increasingly adopting technology and innovations. In addition, the sector is witnessing the emergence of a brand-new breed of private mobility companies that are transformative to the conventional public-sector provided mobility (for example, Uber, DiDi). Transformative Technologies in Transportation11 The Four Categories of Technological Innovations in the Transportation Sector New transportation systems and services are supported and propelled by some major technological advances that accelerated over the last decade. A quick review of these technological innovations would help to better understand the potential and bottlenecks of emerging transportation services and management innovations, and their impact on the economy and society. These innovations can be grouped into the following four categories: Ubiquitous and High-Speed Connectivity Wireless communication technologies play a key role in connecting travelers, service providers, and system operators. The wide deployment of mobile networks and the increasing market penetration of smartphones are critical enablers to many recent technological and management innovations in the transportation sector. A common feature of many new transportation services is the user- centric and demand-responsive design. To customize the services based on individual needs, users need to share when, where, and what services are needed, and service providers need to respond quickly. The TNCs, representatives of the MaaS model, are growing with the deployment of the 4G network. Travelers routinely use their smartphones for navigation, booking services, and monitoring the service delivery in real time. The 5G technology for wireless communication, which promises much higher transmission speed, greater bandwidth, and lower latency, will further enhance the existing services and bring new services from proposal to reality. For example, many researchers believe 5G could accelerate the deployment of CV systems, which allow communication between vehicles (V2V), vehicles and pedestrians and bikes (V2X), and vehicles and infrastructure (V2I) to enhance the safety and efficiency of the transportation system. For remote areas where the communication infrastructure may be lagging, Starlink, which is now providing high-speed internet access across the globe through a network of satellites, offers another exciting possibility. Digital Platforms Driven by Data Analytics and AI To provide customized services effectively, service providers need to enhance their environmental awareness and foresee future conditions. The increasing penetration of smartphones and the expansion of mobile network not only supported the communication between travelers and service providers, but also deployed billions of mobile sensors worldwide. These mobile sensors are generating huge amounts of data every day, which reveal not only individual travel and activity patterns, but also the system performance for the entire network on a continuous basis. Many digital platforms have been developed to harness the power of this continuous data stream and enable and improve transportation services. The recent deployment of big data analytics and AI technologies has taken the power of such digital platforms to a new level. Information of traffic network can now be derived in near-real time and for a large network. Map services such as Google Maps rely on such information to provide real- time congestion map and best-route advice. TNCs such as Uber and Lyft use such information to dispatch drivers and set prices. Logistics companies such as UPS and FedEx use the information to optimize delivery routes and estimate delivery times. Traffic management agencies are increasingly using such data in addition to the conventional sensors to improve traffic operations and planning. Transformative Technologies in Transportation12 The information has also proved valuable for fighting the pandemic. The Sustainability Mobility for All initiative (SuM4All), which unites influential international organizations and private companies for international cooperation on issues related to transportation and sustainable mobility, concluded that continuous, secure, and ethical data sharing will help create a dynamic and responsive mobility system that can address significant societal and environmental challenges (Vandycke and Reja, 2020). An important dimension of digital platforms is their modularity and scalability, which allows the integration of various services from both supplier and user standpoints. For example, MaaS providers can progressively add accessible services to their platforms, such as a TNC app including shared bicycle fleets, scooters, or other forms of micromobility, helping reduce travelers’ carbon impact. Additional services in related domains can similarly be added (for example, delivery services to a MaaS platform), leveraging the power of networked systems. In addition to monitoring the present system performance, AI has also been used to explore the rich history embedded in the data to predict the future, which could inform operations and infrastructure investment decisions of both private and public sectors. Government agencies can manage traffic proactively and improve their long-term planning. The possibilities are not limited to the transportation sector but can extend to many other aspects of land use and service provision, as human mobility is the common driver of many investment and service-provision decisions. Automation Automation includes both the autonomous ground vehicles and unmanned aerial vehicles (drones). From the perspectives of both academia and industry professionals, AV is the single most transformative technological innovation in the current transportation R&D landscape. AV is an integration of many cutting-edge technologies ranging from advanced sensing technologies and high-resolution digital maps for situation awareness to software and hardware for automated vehicle control. It also relies on advanced human factors’ research for the design of driver interfaces. The level of automation varies based on the degree to which AV technologies replace human functions. Lower-level automation can support automated car following and lane control, which has been deployed in commercially available vehicles such as some Tesla models. Vehicles with high-level automation such as those produced by Waymo, a subsidiary of Alphabet Inc., are still under test or operate under a tightly controlled environment. It is generally accepted that AVs will be launched, and the only question is when, although the predicted/promised dates have been missed multiple times in the past. High-level automation will bring radical changes to the way vehicles, infrastructure, and the transportation system are designed and operated. For example, the electronic signage and intersection control system will replace the current physical signage and traffic signals. Curb-side parking spaces and parking lots at the city center may no longer be needed as vehicles can navigate themselves to more remote and cheaper parking spaces. Dedicated AV lanes or roads may be created to separate AVs from manually driven vehicles to enhance efficiency and safety. Many of these options carry huge price tags and the initial choices may have significant and lasting impacts on future options. Transformative Technologies in Transportation13 Alternative Energy for Decarbonization Energy technologies are critical for achieving the decarbonization goal of the transportation sector. The development of energy technologies, most noticeably, the alternative fuels for vehicles, is closely connected with other technological and management innovations. Decarbonization and pivoting to more sustainable energy sources are not limited to one transportation mode. The product line of Tesla has evolved from electric passenger vehicles to sport utility vehicles (SUVs) and pickup trucks. In the air industry, Airbus-Boeing are working on prototypes powered by hybrid hydrogen-electric engines, and the International Air Transport Association (IATA) predicts “electric engines are the future for aviation” (ALG, 2020). An all-electric container ship took its maiden voyage in Norway in late 2020. The ultimate goal is to power large container ships for international trade, one of the largest carbon contributors, with all-electric engines. The battery technology itself relies heavily on science and engineering advances in labs. However, the success of its deployment is tightly connected to the transportation system. For example, without a major breakthrough in battery technology, the convenience of EV usage relies heavily on the construction and deployment of rapid recharging stations, and the corresponding improvement to the electric grid. The construction of charging infrastructure is also critical for the user basis, and, in turn, for driving down the per unit price through mass production. To achieve this virtuous cycle, the planning and construction of infrastructure plays a critical role. Image 1.1. New energy sources concept Source: Adobe Stock. Transformative Technologies in Transportation14 There are also other competing energy technologies. For example, natural gas vehicles have been popular among buses in some regions, although they still burn fossil fuel. Hydrogen-powered, FCEVs are another alternative that is under development. However, it has not been deployed on a large scale because of the high costs and the lack of refilling stations. Adoption of alternative fuels, and electric and plug-in hybrid vehicles, has been the subject of much research and policy analysis. A combination of disincentives to gasoline-powered vehicles and incentives for green alternatives has been tested for at least the past decade, with mixed results. Range anxiety has been a major obstacle for EV adoption, though recent advances in both range and fueling station deployments have alleviated that concern, as evidenced by the success of Tesla and the greater focus of legacy automakers on electric and hybrid electric vehicles. Feebates and rebates have also been pursued with varying degrees of success. Overall, we appear to be at the cusp of a major increase in uptake in the coming years, especially when combined with autonomy and connectivity. Organization of the Report Chapter 1 has set the stage for the discussion on transformative technologies in transportation sector, and presented the aim of this report, which is to analyze the impact of transformative technologies on development issues to provide technology leapfrog opportunities for developing countries. The report will analyze the issues of how technology innovation can address existing challenges of transportation externalities including congestion, safety, sustainability, social equity, resilience, as well as challenges to the overall development, such as access to jobs, efficiency, and crucially, decarbonization. Chapter 2 reviews and analyzes the emerging technological trend from the infrastructure side, where public sectors usually play a stronger role. It covers the physical environment of connected infrastructure, vehicles, and road users. It also examines how the massive amounts of data generated can be augmented by AI to harness the power of the conventional and emerging ITS, and achieve higher efficiency, safety, equity, and sustainability objectives. Chapters 3 and 4 respectively address the personal mobility and freight mobility improvement potential, powered by a connected infrastructure. In both instances, the essential function of marketplaces that match supply and demand is being reinvented through online-distributed software apps that enable wide access to a broad range of users and suppliers, while more efficiently directing resources and deploying assets to meet existing demands. In the process, existing stakeholders and regulatory structures are being challenged, tested, and upended. To the extent that institutions play a critical role in making adoption and deployment possible at scale, it is important to discuss the institutional landscape for transformative transportation technologies, from encouraging technological adoption, supporting adaptation to local conditions, identifying financing options, to deployment and user adoption. Chapter 5 discusses related institutional issues, as well as the additional policy leverage that may be provided in connection with the transformative technologies. Transformative Technologies in Transportation15 References ALG Global Infrastructure S.L.U. 2020. “Rethinking the airport business: Strategic guide on structural changes in the sector.” December 2020. Erhardt, Gregory D., Sneha Roy, Drew Cooper, Bhargava Sana, Mei Chen, and Joe Castiglione. 2019. “Do transportation network companies decrease or increase congestion?” Science Advances. Vol. 5, No. 5. https://www.science.org/doi/10.1126/sciadv.aau2670. Qian, Xinwu, Tian Lei, Jiawei Xue, Zengxiang Lei, Satish V. Ukkusuri. 2020. “Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data.” ScienceDirect. Sustainable Cities and Society. Vol. 55, 102053. https://www.sciencedirect.com/science/article/ abs/pii/S2210670720300408. Schrank, David, Bill Eisele, and Tim Lomax. 2019. Urban Mobility Report 2019. Texas Transportation Institute. Corporate Contributor(s) : United States. Department of Transportation. University Transportation Centers (UTC) Program; Texas. Dept. of Transportation. https://rosap.ntl.bts.gov/ view/dot/61408. U.S. Department of Transportation (US DoT). 2020. “Pathways to the Future of Transportation: A Non-Traditional and Emerging Transportation Technology (NETT) Council Guidance Document.” Office of the Secretary of Transportation. July 2020. https://www.transportation.gov/sites/dot. gov/files/2020-08/NETT%20Council%20Report%20Digital_Jul2020_508.pdf Vandycke, Nancy and Binyam Reja. 2020. “Decarbonizing transport: Building momentum in the run-up to COP26.” World Bank. https://blogs.worldbank.org/transport/decarbonizing-transport- building-momentum-run-cop26. Wilde, Robert. 2019. The Railways in the Industrial Revolution. May 28, 2019. https://www. thoughtco.com/railways-in-the-industrial-revolution-1221650. 2 Smart and Sustainable Infrastructure: Unlocking the Power of Digital Platforms Digital technologies are driving significant changes as transportation infrastructure continues to evolve Smart infrastructures help governments boost their existing transportation infrastructure portfolios to their full potential and improve the sustainability of the transportation system. They also help governments rethink transportation infrastructure and achieve sustainable developmental goals before shaping the physical infrastructure network. Conventional Intelligent Transport Systems Applications 1 Advanced Traffic Management Systems 2 Advanced Traveler Information Systems 3 Commercial Vehicle Operations 4 Advanced Public Transportation Systems 5 Advanced Vehicle Control Systems Integrated System Actuators Sensors, controllers, and actuators form an Controller Layer Enable implementation of interconnected dynamic Sensors control strategies, influencing system, enabling continuous The brain of the system, processing user and service provider learning and adaptation sensor data, using advanced behavior through AI Essential for smart road algorithms for traffic prediction systems, they collect traffic and optimal strategies data, environment information, and system operation data Architecture of Smart Infrastructure Emerging Intelligent Transportation Systems Applications Smart Mobility Systems Smart Asset Management Use data analysis to enhance prediction and Identifies infrastructure issues, prioritize maintenance, personalization and facilitate road safety Smart Traffic Signals Equitable Pricing Models Use real-time tech to minimize delays and optimize Encourages efficient road usage, incentivizes public transit eco-friendliness, and predicts conditions Smart Transit Systems AI Deployment Use AI for fleet management, transit data collection, Leverages tech for traffic information, detects risk, and risk analysis and more reduces wait time for travelers Policy Implications Breaking institutional barriers and enhancing capacity building Empowering smart infrastructure through collaborative data policies Harnessing the power of data and value of digital infrastructure for smart transportation systems Embracing the power of AI and staying open-minded Leveraging the opportunities offered by private-public partnership Transformative Technologies in Transportation18 Chapter at a Glance Connected and smart transportation infrastructure serves as the cornerstone to bring innovative passenger and freight mobility services into reality. Across the world, public sectors usually play the leading role in infrastructure provision. This chapter explores the emerging technology innovations in transportation infrastructure, with a focus on ITS, which is the primary platform for delivering innovative, tech-powered mobility services. It discusses service innovations, the leapfrog challenge, and opportunities for developing countries to adopt a more sustainable development pathway for infrastructure development by leveraging digitalization. It also highlights a few emerging ITS applications and potential policy initiatives that can help transform the transportation sector and achieve developmental goals. Transformative Technologies in Transportation19 Context Major expansions of transportation networks have been associated with economic growth and social change. While the rapid expansion of the rail network in Britain became a powerful enabler of the industrial revolution, on the other side of the Atlantic, the transcontinental railways helped “give the United States the single largest market in the world, which provided the basis for the rapid expansion of American industry and agriculture to the point where the U.S., by the 1890s, had the most powerful economy on the planet” (Brands, 2019). Similarly, the rapid expansion of automobiles enabled by Henry Ford’s car manufacturing innovations created the American middle class and shaped its lifestyle. About half a century later, this trend was reinforced by the construction of the Interstate System, which helps support the vibrant U.S. economy to this day. This path for development has been replicated by many counties in the world. By 2017, the highway network in China reached 4,338,600 km, representing a growth of 8.3 times over 1980 (Jin and Chen, 2019). This rapid expansion of transportation infrastructure has been a major driving force of the unprecedented economic growth during the same time period (Roberts et al., 2012). Now, improving transportation networks and services has become even more critical for development, as it provides key access to markets and talents, particularly for developing countries that are historically lagging in transportation infrastructure. The improvements are no longer just focused on the expansion of road capacity, but more importantly, on using the existing system more efficiently. This change is essential as many other issues related to the transportation sector (congestion, pollution, energy consumption, GHG emissions) are now becoming increasingly challenging. According to TomTom Inc.,7 the top five most congested cities in the world are located in developing countries. Smarter growth strategies are needed to meet the growing mobility needs in these countries. Digital technologies are bringing revolutionary changes and transforming the transportation sector by creating new opportunities for developing smart, affordable, and sustainable mobility solutions. The digitalization of transportation infrastructure includes equipping transportation infrastructure such as the road network, railway, transit stations, airports, and ports with sensors that collect traffic information and system status in real time. It also includes leveraging the massive data streams generated by connected vehicles and travelers. This requires analytics and AI techniques, along with the data-processing capacity to implement and deploy these capabilities. The sensing, information-processing, and strategy-implementing components work together to transform the conventional transportation infrastructure into a smart one to generate new opportunities for efficient, equitable, and sustainable mobility solutions. Several studies have indicated that transportation infrastructure investment is usually positively correlated with economic growth. A World Bank project on “Infrastructure and Growth: A Multi- country Panel Study” (Canning and Bennathan, 2000) found an output elasticity of 0.09 for countries in the middle quartile of incomes, compared with 0.05 and 0.04 for countries in lower and higher quartile of incomes, respectively. In another study, Roberts et al. (2012) concluded that the aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 because of the expressway network construction. Bayen and Shastry (2017) claimed that the cost to acquire and install ITS technologies is “roughly 5 percent of the overall construction budget if installed during construction and the return on investment, measured in safety, travel time reliability, throughput, and quality of life, takes less than six months in highly congested corridors”. An analysis shows that real-time transit traveler information can increase ridership from 40 to 70 7 See TomTom Traffic Index at https://www.tomtom.com/en_gb/traffic-index/. Transformative Technologies in Transportation20 percent, while displaying transit travel times and departure information on highways can lead to a 1.6 to 7.9 percent mode shift from cars to transit (Hatcher et al., 2017). Although the numbers vary, the consensus is that ITS applications provide good return on investment. As governments struggle to provide the necessary mobility for social and economic development, a smarter and more sustainable strategy is needed for transportation infrastructure. While the demand for greater mobility is growing, policy makers are recognizing the various negative social and environmental impacts attributed to the transportation sector. Transportation network expansions help fuel urban sprawl (Nechyba and Walsh, 2004), and induced travel demand contributes to higher traffic congestion, pollution, and GHG emissions. Traffic jams are more prevalent in developing countries (World Bank, 2017); for example, São Paulo, Brazil experiences the world’s worst traffic jams, where commuters suffer from two to three hours of delay daily. In 2020, over 1.35 million lives were lost in traffic crashes globally, which translated to almost 3,700 deaths a day, and around 93 percent of these fatalities occurred in developing countries (World Health Organization, 2018). Worldwide, transportation is responsible for 24 percent of direct CO2 emissions from fuel combustion (International Energy Agency [IEA], 2021). As governments struggle to provide the necessary mobility for social and economic development, a smarter and more sustainable strategy is needed for transportation infrastructure. The integration of conventional transportation infrastructure with ICTs, loosely defined as ITS, was put forward as a promising strategy more than three decades ago. Conventional Intelligent Transportation Systems and Their Evolution Transportation infrastructure is the system designed to facilitate the movement of goods and people. Although the full extent varies from country to country, it conventionally only refers to the physical system. This narrow scope of transportation infrastructure has been constantly challenged over the last few decades as digital technologies have begun to play an increasingly central role in the transportation ecosystem. ITS, which enhances both safety and mobility by integrating ICTs with the physical transportation infrastructure, started to gain momentum from the 1990s. During the first wave of ITS development, the focus was on integrating transportation infrastructure with ICT. Applications were usually driven by government agencies and different subsectors worked in silos most of the time. ITS applications were organized by functional subsystems, namely ATMS, ATIS, commercial vehicle operations (CVO), APTS, and AVCS. Many improvements were made through these programs. For example, automated vehicle location (AVL) technologies were introduced by transit operators to provide real-time bus location and support the APTS. Sensors were widely deployed to feed traffic information to actuated signal control systems. Some features of ATMS and ATIS, such as dynamic message signs, ramp metering systems, and traffic management centers, were also widely deployed. However, despite heavy investment in ITS hardware by many developed countries, some of the more ambitious objectives under the ITS architecture were never achieved. The development of a fully automated highway system, a core objective of many early ITS initiatives, was not achieved despite various pilot projects. Some researchers argue that the higher throughput achieved by local ITS strategies, such as actuated signal control systems, only moves the queue to the next bottleneck and does not really reduce congestion. A part of the reason the full potential of ITS may not have been attained is the focus on hardware deployment without accompanying investment in developing and testing the intelligence at scale, as well as the lack of integration of ITS deployments across modes. Transformative Technologies in Transportation21 The public sector is traditionally responsible for the provision of transportation infrastructure and most ITS-related investment decisions. Since the 1990s, most ITS research and development initiatives in the U.S. have been championed by the Federal Government (Auer et al., 2016). The Colorado DoT manages 2,938 ITS devices installed on the roadways, as well as 1,237 ITS network devices installed in the Node Buildings, and 1,600 miles of fiber optic cable statewide, all of which constitute the backbone of the ITS in Colorado (Colorado DoT, 2022). However, the landscape has changed in the last 10 years; bottom-up innovations from the private sector now play a leading role in the development and deployment of transportation technologies and the associated services. Currently, most MaaS solutions are developed and provided by the private sector. In the past decade, the development of traditional ITS solutions have gradually led to the construction of smart roads. The concept of “smart” differs from traditional ITS by three major characteristics: people-centric, data-driven, and powered by bottom-up innovations (Chen, Ardila-Gomez, and Frame, 2017). Although this trend is catalyzed by the rapid expansion of mobile telecommunication networks, increasing penetration of smartphones, and innovations in AI, it is the integration of these technologies toward new solution approaches that makes this wave of changes transformative. Image 2.1. Smart road car with artificial intelligence combine with deep learning technology Source: Adobe Stock. Transformative Technologies in Transportation22 The scope and major functionality of smart roads are yet to be further defined. For some researchers, smart roads integrate physical transportation infrastructure with sensors so they can “feel” vehicles like fingers on a touchpad (Al-Qadi et al. 2004). For others, smart roads should also include energy solutions such as using the right of way for solar power panels, adjusting streetlights interactively to boost sustainability, or harvesting the pavement vibration for power generation (Gnatov, Argun, and Rudenko, 2017). At a minimum, smart roads augment conventional transportation infrastructure with ICT so it can assess real-time traffic condition and system status through a range of sensors, develop better operational strategies, and implement the strategies by either feeding system users with information or regulating the traffic directly. Its three major components include sensors, controllers, and actuators, as shown in figure 2.1. Figure 2.1. Architecture of a Smart Road System S nsor L r Controll r L r Actu tor L r Volum Sp d D nsit R wD t Humidit Visibilit ... Pr diction & Optimi tion Source: World Bank. Sensors Sensors are the eyes and ears of a smart road system. Travel trajectory data from mobile phones and in-vehicle GPS have been increasingly used to derive traffic information. For example, companies such as INRIX, TomTom, and HERE provide information on factors such as traffic flow and travel speed, and show great advantage in coverage and cost. Data fusion and integration technologies may help users make the most of data from both mobile network and fixed sensors to collect traffic information. Table 2.1 provides a quick review of commonly used sensors. In addition, environment data such as temperature, humidity, and visibility still have to be collected by local sensors. Sensors also collect information about system operations such as the status of traffic signals, loading factor, location of transit vehicles, and toll rate as well as level of usage of toll roads. Although they may not look futuristic, their applications are still lacking in many developing countries. Enhancing the data collection capability is the critical first step toward the development of smart infrastructure in developing countries. Transformative Technologies in Transportation23 Table 2.1. Summary of Sensors Commonly Equipped for Transportation Infrastructure Types of Sensors Applications Cost Magnetic loop Traffic volume, density, speed monitoring Low Infrared sensor Speed measurement, vehicle length, volume, lane occupancy Low Pneumatic road tube Vehicle classification, vehicle count Low Ultrasonic sensor Traffic volume monitoring Low Bluetooth Travel time, traffic volume estimation Low Mobile phone Travel time, traffic volume estimation, passive travel survey Low Radar Traffic speed monitoring Middle Automatic vehicle Monitoring of transit fleet Middle location system Traffic volume monitoring, incident detection, Camera High automatic toll collection Laser Traffic volume monitoring High RFID (Radio Frequency Automatic toll collection, monitoring of hazardous cargos, High Identification) supply chain management In-pavement sensor Weigh-in-motion, pavement conditions High Source: World Bank. While the higher costs of sensors and other ICTs have historically hindered their adoption in developing countries, their value has gradually been recognized. ICT components were part of the World Bank’s Regional Transport, Trade and Development Facilitation Project aimed at improving the movement of goods and people along the Lokichar-Nadapal corridor in Kenya. ICTs were also an important component of the Western Economic Corridor and Regional Enhancement Program in Bangladesh and the Regional Connectivity and Development Project in Azerbaijan. Although all three projects emphasized the deployment of optical fiber network to support digital connectivity and data transmission, the type of sensors deployed can be further explored. It is imperative to build up the sensing capability for transportation infrastructure in developing countries. This process can also be accelerated by adopting the “Dig Only Once” policy, which encourages the installation of telecommunication infrastructure to ensure connectivity and reduce disruptions. Glickman (2022) estimated that this policy can achieve cost savings of 33 percent. Another study by the World Bank (Strusani and Houngbonon, 2020) shows that adding broadband network with road construction only adds 0.9 to 2 percent of the overall cost of a road, but can significantly reduce capital costs later and improve the operating efficiency, reliability, and safety. Broadband InfraCo in South Africa is an example of early success. Controllers The controller layer is the brain of the smart road system. It first processes the raw data collected by sensors and translates them into a continuous data stream. Advanced algorithms are then applied to analyze this data stream and predict future traffic conditions, among other useful system information. Based on the prediction, the system can derive better traffic operation strategies leveraging the specific goals selected by the management agencies. Depending on the size of the network and the level of accuracy required, the prediction of future traffic conditions and derivation of optimal strategies could be very challenging. Transformative Technologies in Transportation24 Recent advances in computing technologies such as cloud computing and AI have the potential to address this challenge. Cloud computing allows transportation agencies to move data analytical tasks from local traffic operation centers to the cloud to benefit from the greater computing power, expanded data storage capacity, in-time software maintenance and upgrade, automatic data backup, and improved resilience to local disruptions such as power outage (for example, Bitam and Mellouk, 2012; Li, Chen, and Wang, 2011; Guerrero-Ibanez, Zeadally, and Contreras-Castillo, 2015). It provides an advanced platform through which AI algorithms can be applied to analyze data and derive optimal operation strategies. Actuators Actuators, which cover all the strategies of management agencies, can be used to influence travel choices (for example, through variable message signs, toll rates, traffic signals). This will enable the implementation of the control strategies to influence the behavior of users and service providers. These strategies need to be implemented by various traffic control and information communication devices, which are usually a part of the existing transportation infrastructure. These implementation strategies cover both the supply and demand sides of the transportation system and include both short-term (operations) and long-term (planning) strategies. Table 2.2 summarizes some relevant examples. Table 2.2. Examples of Traffic and Travel Demand Management Systems Supply Side Demand Side • Traffic signal coordination system • Congestion pricing system • Automatic traffic incident detection and • Ramp metering system clearance system • Advanced traveler information system • Queue detection and warning system • Parking information system • Dynamic transit fleet management system • Dynamic tolling system • Transit route optimization Source: World Bank. Sensors, controllers, and actuators do not act in isolation but in an integrated dynamic system. The strategies implemented by the actuators affect the system status. The sensors detect these changes and feed them into the controller. The controller analyzes the new system dynamics and makes recommendations for the next time period. This capability of continuous learning and adapting is essential to a “smart” system, which can be enabled by AI. Emerging Intelligent Transportation Systems and Their Applications Digitalization of transportation infrastructure takes smart roads from a concept to an unfolding reality. For HICs, smart roads help governments boost their existing transportation infrastructure portfolios to their full potential and improve the efficiency and sustainability of the transportation system. For developing countries, they help governments rethink transportation infrastructure and invest wisely to achieve sustainable developmental goals before the physical infrastructure network is shaped. The combinations of sensing, computing, and implementing capacity offered by the three Transformative Technologies in Transportation25 components of the smart road framework discussed above also give government agencies the opportunity to customize smart mobility solutions based on local conditions. This section presents a few examples of such opportunities. Smart Mobility Systems A smart mobility system can synthesize mass data from a diverse set of sources, and quickly provide optimized and customized mobility solutions to a large number of users simultaneously. The ITS center is usually the platform for synthesizing the data and delivering the services, which is constantly augmented by emerging technologies (see Box 2.1). The digital twin, which is the digital model of a physical system that helps analyze and predict the system conditions, is usually developed to identify optimal solutions. Assisted by the digital twin, the smart mobility system can propose alternative travel modes, departure time, routes, and destinations based on individual needs and help users better understand the benefits and costs, including the environmental ones, associated with each option. The system can become even smarter by providing incentives such as a small subsidy for transit fares or gas credits for avoiding peak hours. This would incentivize people to try options outside of their daily routines and help them establish new travel habits that are more beneficial to the society. Thus, the system can move the needle by changing travel behavior, one person at a time, with minimal cost and minimum enforcement. Collectively, the efficiency improvement and environmental benefits could be huge. IncenTrip8, a travel incentive program developed by researchers in conjunction with an intergovernmental agency in the U.S., is a pilot in this direction. The program aims at accomplishing significant system-level benefits by influencing individual travel behavior through a monetary reward program. The ability of such systems to achieve ambitious system-level goals remains to be established. Image 2.2. Paying contactless with smartphone in public transport Source: Adobe Stock. 8 For more information on IncenTrip, see Maryland Transportation Institute’s website at https://mti.umd.edu/incentrip. Transformative Technologies in Transportation26 Box 2.1. The Evolution of ITS Investments in Wuhan City, China Wuhan, the capital city of Hubei Province, is a commercial center and transportation hub in Central China. Hosting more than 13 million residents, the city sprawls over 8,000 km2 area. The World Bank has been supporting the city in tackling its urban transportation challenges with three investment projects over two decades. The evolution of the use of technology, specifically, ITS, shows a progression from individual locations to city-wide interventions, from isolated equipment to integrated system, and from hardware (the “gadgets” that collect data) to software (the “brain” that analyzes data and support decision-making). The first project has engendered experiences in implementing ITS, the second project fostered replication and expertise in the better integration of ITS, and the third project uses the accumulated experiences and capacity as a springboard to enable the comprehensive use of data and contribute to Wuhan’s smart city initiatives. The First Wuhan Urban Transport Project financed by the World Bank was completed in 2010 with an ITS component in traffic signal and control center. Specifically, the project installed new and upgraded traffic signals at 274 junctions, with communication equipment, as well as equipment for two traffic control centers. Area traffic control (ATC) systems were implemented in center city districts while traffic signals operating under isolated control were implemented in periphery areas. This component also includes the installation of traffic monitoring cameras and variable message signs to better manage cross-river traffic. The Second Wuhan Urban Transport Project was completed in 2018. Besides optimizing traffic flow, the ITS investments also aimed to prioritize public transportation and improve road safety by supporting: • The traffic signal upgrading to the state-of-art ATC system, including bus priority traffic signals and mid-block traffic signals for pedestrians. • The installation of traffic monitoring cameras, including over key bridges, and overhead cameras on bus priority lanes. • The upgrading of the traffic control center (including video and audio systems). • The installation of ₀ Variable message sign (VMS) ₀ Weigh-in-motion (WIM) system for truck management and control ₀ Fiber-optic communication network The ITS investments on transit signal priority and timing optimization as well as bus lane enforcement during peak hours increased the efficiency of bus operations (see figure B2.1.1). The signal control, traffic monitoring, and the management and enforcement system in the control center contributed to improved road safety for all users, especially pedestrians and cyclists. In addition, the river-crossing monitoring system facilitated smoother traffic and quick clearance of incidents, thus alleviating congestions on river crossings. Transformative Technologies in Transportation27 Box 2.1. The Evolution of ITS Investments (Cont.) Figure B2.1.1. Bus Priority Lane and Traffic Control Center, Wuhan Source: World Bank, 2018, Implementation Completion and Results Report for Wuhan Second Urban Transport Project. The third World Bank loan-financed urban transportation project is still under implementation. ITS investment now has the following new characteristics: people-centric, data-driven, and powered by bottom-up innovations. The project provides support to each level of the city- wide smart mobility framework: (a) front-end traffic information collection systems; (b) transportation planning and policy support center including a transportation data repository and a decision-making platform; (c) traffic monitoring and management system by the traffic police/traffic management bureau; (d) external traffic monitoring and management system by the transportation bureau; and (e) smart parking management system by the parking company (see figure B2.1.2). Significant technical assistance is also provided in traffic modeling and simulation for planning and policy making, big data integration, and data sharing including data security and privacy. Figure B2.1.2. Stylized Scheme of Wuhan Transport Planning and Policy Support Center . Source: World Bank-loan financed Wuhan Integrated Transport Development Project-Project Management office. Transformative Technologies in Transportation28 Smart Traffic Signals On arterial streets, the smart signal system is a perfect example of the sensor-controller-actuator model. Fixed-time signals use constant control parameters such as cycle length; and green, yellow, and red time for different approaches. It is easy to implement, and maintenance costs are relatively low. However, it does not respond to the dynamic traffic flows and may lead to unnecessary delays. In contrast, an actuated signal control system monitors incoming traffic to an intersection continuously and adjusts the corresponding control parameters to minimize the total delay. In addition, it may also respond to pedestrian and other road user requests (for example, through push buttons or camera sensors) and adjust signal plans to provide safe pedestrian crossing. The actuated signal system, which adopts adaptive control strategies, requires the deployment and maintenance of multiple sensors and the computing capacity for optimization (which is minimal for an isolated intersection). Its deployment is now common in developed countries and its numbers are increasing in developing countries (see Box 2.2). However, it becomes more challenging to expand the control objectives and signal coordination areas. Transit signal priority (TSP) expands the capacity of the signal system to prioritize transit vehicles and helps improve the travel speed and reliability of public transit (usually through extended or early green lights). To detect the presence of transit vehicles, additional sensors and more sophisticated optimization programs are required. Although sensor deployment is challenging, it may be beneficial under certain conditions, as the system would become more resilient to abnormal conditions like inclement weather. A recent U.S. study (Anderson, Walk, and Simek, 2020) found that 28 of the 46 surveyed transit agencies had active TSP deployments, and another 13 transit agencies are either in pre-deployment testing or have plans to pursue TSP in the future. Image 2.3. SmartCycle Bike Indicator Source: Adobe Stock. Ambitious AI companies believe the current smart signal systems are local in scope (Austin, 2019). Extending the system to cover the entire city and connecting most vehicles to the system could provide higher benefits. Researchers have put significant efforts into developing traffic-responsive signal timing algorithms in the last decade. Recently, as an alternative to conventional model-based algorithms, AI-based methods have been tested on traffic signal timing problems and have shown promise. Particularly, many existing studies (Aragon-Gómez and Clempner, 2020) have developed deep reinforcement learning models to optimize traffic signal timings at urban intersections. Transformative Technologies in Transportation29 Box 2.2. Smart Signals: The Traffic Lights Systems with 5G in São Paulo, Brazil The city of São Paulo conducted a study on benchmarking and analysis of future technologies for the modernization, expansion, and adaptation of the traffic lights systems in relation to the advent of 5G technologies. The aim was to ease the daily life and social interaction of inhabitants of the most populous city in Latin America, as well as further advance economic and ecological development in a sustainable manner. As one of the few megacities in the world, São Paulo has an extensive urban mobility network with 20,000 km of roads and the largest bus network in Latin America. However, its traffic lights system operates less efficiently. None of its 5,886 traffic lights operate in traffic- responsive or adaptive manners, nor do they have the ability to prioritize buses or emergency vehicles. Moreover, the local traffic authorities suffer due to the theft and vandalism of cables and controller cards, which demand large amount of maintenance resources, as well as the unstable power supply. Figure B2.2.1. Infrastructure for the Cooperative-ITS (C-ITS) Source: World Bank Smart Mobility Program for São Paulo and Salvador. To improve traffic operations with less burden of maintenance, the World Bank supported a study that proposed a three-step plan to modernize, expand, and adapt São Paulo’s traffic lights system with the integration of advanced 5G communication technology (see figure B2.2.2). The first step is attaining state-of-the-art technology by converting existing fixed control traffic lights to traffic actuated or adaptive ones and allowing the prioritization of public transport, active transport, and emergency vehicles. Up to a 30 percent reduction in travel time is expected with the completion of the first step. The second step involves the use of low-latency, high-bandwidth 5G communication to enable wireless sensorization. This would significantly reduce the total investment in the installation and maintenance of equipment since long pipelines and cables will not be needed to the extent required in the previous step. The third step aims at preparing the infrastructure for the cooperative-ITS (C-ITS) by means of installing road-side units (RSUs) and implementing solutions to get floating car data (FCD), so wireless strategies for pedestrians and bikes can also be possible (see figure B2.2.1). The whole implementation will take 13 years. Transformative Technologies in Transportation30 Box 2.2. Smart Signals: The Traffic Lights (Cont.) This plan is expected to reduce congestion and travel time, and further reduce the emissions of GHG. The monetarized net benefit is estimated at about $4.3 billion. Figure B2.2.2. Three-step Plan to Modernize São Paulo’s Traffic Lights System Years Stage 1 1 State-of-the-Art Controllers + Priority + Local optimization Reaching • Planned Modernization of controllers 2 • Coordination optimization $$$ • More plans for flexibility during the day and the week • Actuated Traffic Scenario 1 Stage 2 • Prioritization of public transport 3 • Bicycle and pedestrian prioritization Adaptive + Central Optimization Traffic Management System (TMS) • Monitoring, maintenance and remote central control (TMS) 4 • Adaptive Traffic • Coordination optimizzation • Network optimization up to 30% reduction in travel times $$$ 5 Stage 3 6 Wireless detection and 5G • Scalability & Harmmonization Sensorization Scenario 2 • Increase in coverage Stage 4 7 • Optimization of the system • Increase in benefits • Begin of digitalization $$ Stage 5 • MaaS 8 Stage 6 9 10 A sustainable City prepared for the future C-ITS Welcoming Scenario 3 • Use of Big Data - City Twin the Future • Air quality managment less stops means, less polllution 11 • Users informed On-Line: Displaying messages in apps or VMS $ 12 13 An integrated 5G & C-ITS Mobility Management System The future is here ready for the future Full Smart 5G TMS • Full digitalization • Prioritization • Autonomy • Analysis of data Future • Operability • Quality • Connectivity • Adaptability • Responsiveness Source: World Bank Smart Mobility Program for São Paulo and Salvador. Note: C-ITS = Cooperative Intelligent Traffic System; MaaS = Mobility-as-a-Service; TMS = Transport Management System; VMS = Variable Message Sign. Transformative Technologies in Transportation31 Smart Transit Systems A growing number of cities have adopted the automated fare collection (AFC) system, which brings a wide range of benefits to transit operators, transportation planners, government agencies, and most of all, transit riders. The AFC system improves the payment process, which reduces the transaction costs and improves the operational efficiency of the transit system, while serving as a gateway to the automation of transit data collection. It gives transit operators full visibility into the transit ridership, level of services, and improves operational performance measures (see Box 2.3). The adoption of AFC systems is accelerating in both the developed and developing countries (Rubiano and Darido, 2019). With AI enabling real-time data and fast decision-making, there has been a surge in new smart transit applications such as road risk analysis, traffic information prediction, route guidance, and fleet dispatching. Innovations are mostly focused on the following key areas (FOXYpreneur, 2019): • Real-time fleet analytics: With the ability to collect real-time data such as traffic patterns, road conditions, and weather information, AI-based applications can help predict crash risks and assist fleet managers to make better decisions in terms of scheduling, dispatching, and routing. • Better repair and maintenance: Understanding and predicting the life cycles of fleet vehicles is important for the responsible agencies to plan repair and maintenance activities in advance and optimally allocate budgets. Using historical information on mechanical faults, service lifetime, and vehicle-miles-traveled, AI models can effectively predict the optimal lifetime for each type of fleet vehicles. • Fleet integration: Large fleet operations often require continuous flow of information and coordination among multiple departments. Traditional fleet integration efforts are usually labor- intensive and lack efficiency. AI-based systems offer new solutions to integrate all information onto a single platform, feed that information to all departments simultaneously, and synchronize the operational activities across departments. Image 2.4. Integrated control system simulation and autonomous driving in smart city Source: Adobe Stock. Transformative Technologies in Transportation32 Box 2.3. Innovation in Fare Collection Systems for Public Transportation in African Cities Mobile money is an alternative currency created by the mobile network for transferring value across the network. In low-income countries with poor banking infrastructure, mobile money is a viable path to financial inclusion. In many African countries, mobile money (for example, M-Pesa in Kenya) is used to pay directly for public transportation trips or to load smartcards for use on public transportation systems. Sub-Saharan Africa represents, by far, the biggest market for mobile money services globally, accounting for $456 billion out of a global total of $690 billion in 2019. Figure B2.3.1. Automated Transit Fare Systems in African Cities Source: World Bank. M-Pesa, which is now used widely as a payment mechanism on Nairobi’s public transportation system, is a great example of technology readiness and innovations based on local conditions. Various cashless payment systems have been implemented in Nairobi since around 2010 but with little traction. The success of M-Pesa relies on a few unique factors. First, using mobile money like M-Pesa does not require a high-end smartphone. The system would work well as long as the phone has a camera and can scan QR codes. Second, the same mobile money can be used for many other payment purposes in people’s daily lives and the transaction is smooth. Third, the usage is flexible and can also be used by informal transit operators. The virtual payment option also increased the resilience of public transit during the COVID-19 pandemic. The data generated by the fare system, which can be used to optimize the overall transportation system and improve bankability, is the most important benefit. Its application in the public transit fare system, in return, attracted more subscribers to mobile money. Therefore, the application of mobile money preceded the wide adoption of fully functioning smartphones in Kenya. Now with the rapid adoption of smartphones and continuous improvements in mobile network, automated transit fare systems may co-evolve with the development of super-apps, and may become the hub for other innovations in transportation. A recent World Bank study (Arroyo-Arroyo, 2021) analyzed the innovations in fare collection systems for public transportation in African Cities (see figure B2.3.1). Transformative Technologies in Transportation33 Smart Asset Management Smart mobility solutions cannot function optimally if the transportation infrastructure is not well maintained. Keeping a complete and up-to-date database on transportation infrastructure is essential. However, such a task is challenging, particularly for assets that are historically not the focus of traffic management agencies. For example, a digital map of sidewalks and curb ramps is critical for identifying access barriers to people with special needs and providing mobility solutions (for example, navigation assistance). Such digital maps are rare. A few researchers (Hara, Le, and Froehlich, 2012; Saha et al., 2019) developed an online platform to enlist volunteers to create a sidewalk inventory and identify accessibility issues using Google Street View data. It showed that untrained crowd workers could identify sidewalk accessibility issues with fair accuracy (about 80 percent on average). A similar concept was adopted by the IBM Sidewalks application (Shigeno et al., 2013). However, such crowd-sourcing approaches, while low on costs, are not reliable. Automated surveyors based on either light detection and ranging (LiDAR) data or automated image processing algorithms are more promising solutions. Kargah-Ostadi, Waqar, and Hanif (2020) presented an AI solution for automated real-time identification of transportation assets from roadway images. The deployment of such mobile surveyors is vital for developing a smart asset management system, which could serve as the backbone of many smart mobility solutions. This smart asset management system could provide the fundamental digital map, identify infrastructure barriers and maintenance issues, and automatically prioritize maintenance needs based on a wide range of policy objectives. Its AI algorithms-based automated framework is particularly attractive to developing countries that historically lack an established data collection practice. Such an asset management program requires a well-trained workforce with advanced knowledge. Furthermore, it holds great promise of leapfrogging opportunities. Smart asset management also relies on the real-time monitoring of asset usage, which necessitates data sharing with other systems such as the smart transit and smart mobility systems. Additional monitoring measures, such as the deployment of a WIM system, are also critical. Assessing road traffic safety and identifying the high-risk spots on the road network are an important task. Many traditional methods use historical crash data to develop statistical models for road safety analysis. However, those methods are usually not applicable in many developing countries with limited crash data. There have been some efforts to explore alternatives to fulfill the same goal; for example, the International Road Assessment Program (iRAP) uses a rating approach to help assess road safety performance (Gold, 2017). With either a drive-through or a video-based approach, raters use specialized software to measure elements such as lane widths, shoulder widths, and the distance between the road edge and fixed hazards, and assign a safety rating for the roads. This rating approach is highly effective for road safety assessment when sufficient crash data are not available. However, it would involve considerable manual efforts by skilled raters. More importantly, variations of rating standards by humans are not avoidable, making the assessment results inconsistent in some cases. Following the same logic, recent advancements in AI can facilitate the process of automatically detecting and rating unsafe conditions on roadways. Rashidi and Markovic (2021) provide examples of using AI-based computer algorithms to assign road safety ratings. Transformative Technologies in Transportation34 As road infrastructure deteriorates over time, transportation agencies have to update their asset inventory frequently. LiDAR and photogrammetry are mature technologies in sensor-based data collection and provide 3D point cloud data that are further processed to classify and count roadway assets by AI software packages. The two applicable data collection modes for road asset maintenance divisions are mobile (sensor mounted on a vehicle) and aerial (sensor mounted on a drone). Their data quality is subject to the sensor platform’s specifications (either laser scanner or digital camera), positioning, altitude, and traveling speed while collecting data. As each of the above- mentioned factors affects data quality, the AI accuracy changes when detecting road assets. Effective roadway slippery condition detection can support early warning of hazardous road conditions and snow removal performance evaluation. Currently, many transportation agencies track the winter severity indices and assess snow removal performance by either indirectly calculating how well their systems meet the level of services or directly evaluating atmospheric and road conditions. Also, the road surface temperature and slippery condition are essential parameters to facilitate hazardous road condition detection and warning. While some real-time weather data might be available from roadside sensors, the more densely distributed traffic camera systems and advanced AI models offer an unprecedented opportunity to detect hazardous road conditions and evaluate snow removal performance in real-time. Equitable Pricing System Mobility is offered as a public good in many countries as most users do not directly pay for the usage. This financing mechanism for public roads encourages excessive demand for travel, resulting in inefficient utilization of roadway capacity and high traffic congestion in many cities (Schrank, Eisele, and Lomax, 2019). The outcome was an estimated 8.8 billion extra hours of travel and 3.3 billion extra gallons of fuel for a congestion cost of $179 billion in the U.S. in 2017. The European UNITE project estimated the costs of traffic congestion to be 1.5 percent, 1.3 percent, and 0.9 percent of the gross domestic product (GDP) in the U.K., France, and Germany, respectively (Nash, 2003). This is not a new problem. Pigou (1920) first proposed to charge a price based on the marginal costs each traveler imposed on the system. The idea of congestion pricing has been expanded by many researchers (for example, de Palma and Lindsey, 2011; Verhoef, Nijkamp, and Rietveld, 1996) to consider various factors and improve its applicability. Despite its popularity in the research community, the implementation of congestion pricing is slow in practice as many practical issues remain to be addressed. To encourage more environment-friendly modes, incentives to use transit, clean-energy, and high-occupancy vehicles (HOVs) have been proposed in conjunction with the pricing system. Some toll lanes in the U.S. have started to offer toll discounts based on vehicle occupancy. The Texas Department of Transportation offers a 50 percent discount to HOVs and motorcycles on express lanes, while applications in other countries are still rare. Implementations of dynamic forms of pricing require real-time data collection and related algorithms. Meanwhile, due to the relatively high costs for toll collection, governments across the world tried alternative methods to manage travel demand, particularly in urban areas with extreme traffic congestion and severe air pollution. For instance, Mexico City imposed a regulation in 1989 banning each car from driving on a specific day of the week according to its license plate number. A similar regulation has been adopted by Sao Paulo, Brazil; Bogotá, Columbia; Quito, Ecuador; Santiago, Chile (Davis, 2008); Beijing, China; Manila, the Philippines; Lagos, Nigeria (Thomson, 1998); Kigali, Rwanda; Transformative Technologies in Transportation35 and Athens, Greece (Kambezidis et al., 1995). Other cities, such as Singapore and Shanghai, China, tried to manage travel demand by regulating the number of vehicles in a city through license plate auction or lottery. However, the “Day without a Car” type of policy may encourage families to buy a second car, while the control of total vehicles in a city through license plate rationing encourages excessive driving among those who did get one. Zhu, Du, and Zhang (2013) showed that the pricing policy, if successfully implemented, is more effective. Successful implementation of a congestion pricing scheme requires sensing, computing, and toll collection capability, all provided by the smart infrastructure system. Sensors will measure traffic and environmental conditions of the entire transportation system in real time, which could be used to create a digital “twin” of the physical system. AI algorithms will be trained to predict the system conditions over the near future, and derive the optimal pricing strategies accordingly. These pricing strategies can be implemented using existing infrastructure, or with the deployment of new infrastructure. The impact of such pricing schemes will be captured by the sensors, which will, in turn, be used to train the AI algorithms for better prediction performance (Dong et al., 2011). This framework is flexible enough to accommodate additional objectives such as environmental cost. The pricing scheme can also consider the environmental footprint of different vehicle classes, occupancy, energy sources, atmospheric conditions, and geographic locations. AI Deployment in Transportation As a booming transformative technology, AI presents a possible avenue for developing countries to leapfrog to meet future smart mobility needs, by leverage existing infrastructures through better utilization of their capacity (see Box 2.4). For instance, AI can leverage existing roadside cameras to collect real-time traffic information at a relatively low marginal cost. Such data can be further used by an AI algorithm (for example, deep learning) to design a more efficient traffic management plan. By analyzing road images captured by onboard vehicle cameras, AI can facilitate the process of detecting and rating risky conditions on roadways, based on attributes such as side slopes, shoulder width, striping, pavement condition, and guardrail usage. The use of AI technologies to optimize the performance of existing assets can be a critical policy lever for developing countries with limited resources. The current deployment of AI-based transportation applications is limited in developing countries. Didi Chuxing, a Chinese private company that provides a mobile app-based transportation services platform, has been using AI algorithms to predict traffic jams to build predictive dispatching models for their ride-share vehicles. Leveraging the huge amount of data collected by the mobile app, Didi Chuxing claims that it can forecast traffic congestion 15 minutes in advance, with 85 percent accuracy (Zoo, 2019). Given the forecasted traffic condition, Didi vehicles will be dispatched to the high-demand areas before the transportation network becomes congested, resulting in reduced wait times for users. How to use AI to gather enriched transportation data, with existing infrastructures and assets, is a critical issue for developing countries to explore. Transformative Technologies in Transportation36 Box 2.4. Five Areas that AI May Transform Transportation (Mahmassani, 2021) • Pattern recognition: As AI can quickly gather and analyze data, it could transform the traditional transportation management approaches by providing pattern recognitions such as the demand for mobility services, operational planning for freight and personal mobility, and information of market segments. • Prediction: Leveraging historical transportation data, AI has great potential to improve the performance of prediction tasks (for example, travel time prediction) through ensemble forecasting, adaptive forecasting, and so on. Those prediction models can also be integrated with control interventions. • Optimization: When conventional optimization models are either difficult to solve or computationally expensive, AI offers alternative approaches to deal with both small- and large-scale transportation optimization problems. Representative methods include AI-based heuristics, statistical learning, and approximate dynamic programming with reinforcement learning. • Control: Classical control theories have been widely adopted in transportation systems and the advancement of AI knowledge could further improve the efficiency and effectiveness of those control models. Such examples in transportation include system- efficient autonomous vehicle operations and dynamic flow management. • Learning/Personalization: To provide better transportation services, AI can personalize recommendations/incentives for green behaviors, enabling platform interactions (for example, mobility on demand). In many developing countries, the main challenge to improving transportation services is around the availability of data. AI can help generate new solutions for data collection by leveraging existing infrastructures in developing countries. For example, cameras are more affordable than other transportation sensors, and roadside cameras have been widely installed in many developing countries. However, most of them are only used for monitoring purposes and the obtained videos are seldom stored. With advancements in AI-based computer vision technology, it becomes possible to use AI to extract valuable transportation data from the videos. An AI toolset that has been implemented to extract data from videos is Yolo (Simony et al., 2018), which is an open-source programming package that can help detect the presence of different objects, including pedestrians, bicyclists, cars, and road signings. In one study, a one-minute road video for the Nyabugogo (KN 1 Road) street in Kigali, Rwanda was used to test AI potential in road user identification. A detection sample is shown in figure 2.2. Transformative Technologies in Transportation37 Figure 2.2. Counting Vehicles, Pedestrians, and Motors in Rwanda using AI Source: Maryland T ransportation Research and Artificial Intelligence Laboratory (M-TRAIL), Research: Computer Vision Application. Link: https://sites.google.com/view/mtrail/research/cv As traffic conditions are always evolving, good travel decisions and management strategies rely not only on the accurate information of the current conditions, but also on the reliable prediction of the future. Digital twins, the models that are created in the digital world to simulate various scenarios in the real world, can help. The digital twin of the transportation system and the analysis capability supported by AI allows system operators to customize travel options for each individual based on their unique travel needs and habits, and try to induce better travel behavior through the proposition of alternative travel options and/or incentives. The key is to identify value propositions by analyzing the multi-modal transportation system as an integrated system and expand user’s options instead of restricting it. Policy Implications The digitalization of transportation infrastructure can potentially bring transformative changes to the transportation system and direct it toward an equitable and sustainable future. As the primary owner and the operator of transportation infrastructure, governments can play a significant role in facilitating and accelerating the process. Moreover, as the primary guardian of the public interest, governments should also be aware of the potential perils in the process and act proactively to ensure maximal social benefits. Breaking Institutional Barriers and Enhancing Capacity Building Transportation system is an integrated multimodal system, yet historically, infrastructure management has been divided by mode, causing government agencies to operate in silos. These institutional barriers hinder efficient collaborations between different agencies, thereby limiting the realization of maximum social benefits. A structural change in existing business models may be needed to achieve the full potential through the adoption and deployment of new technologies. Developing smart infrastructure requires a full review of sensing, processing, testing, and implementing capabilities. Interinstitutional collaboration is required to facilitate the deployment Transformative Technologies in Transportation38 and unlock the full potential. With limited resources, intersectoral coordination is needed at different stages, including planning, development, operations, and maintenance, to maximize benefits. Regulatory tools need to be adapted so technologies can be efficiently and effectively deployed. For example, the adoption of smart drivers’ license in Kenya requires close collaborations between the police department and transportation agencies. Similarly, implementing the smart port system in Busan, Korea, needs collaborations among customs, maritime management, and conventional transportation agencies. Establishing an interagency coordination mechanism can streamline technology deployment in such cases. In addition, due to the rapidly evolving landscape of digital technologies, capacity building is essential to update the departmental knowledge base and foster creative thinking. As an example, the World Bank recently provided a 10-year, $400-million loan to Serbia, aiming not only to improve the railroad infrastructure but also to strengthen the institutions and develop a workforce that oversees key rail projects and address the country’s air quality challenge (Aragones and Vukanovic, 2021). Similar efforts can also be encouraged in other smart infrastructure projects. Harnessing the Power of Data and Value of Digital Infrastructure for Smart Transportation Systems The power of data is reflected by the information and knowledge derived from it. To transform data into actionable information, it is crucial to use it to train algorithms and build a digital twin of the physical infrastructure system that could serve as the testbed of different management strategies and policies. This digital infrastructure is the soul of the smart infrastructure system and is essential for a wide range of applications such as real-time monitoring and management, predictive maintenance of transportation infrastructures, optimization of operations for transportation systems, intelligent decision support, and testing and validation of new technologies, services, and policies. Digital infrastructure unlocks planning opportunities and just-in-time system coordination by generating massive data from interactions among the user, service provider, and infrastructure. It can also learn from past operations and make improved recommendations on a continuous basis. Moreover, developing countries are not limited by their legacy transportation systems when it comes to implementing smart infrastructure. Conventional transportation infrastructure can evolve into smart infrastructure when equipped with sensing, computing, and implementation capacities. The costs of equipping existing infrastructure with ICTs are marginal compared with building new infrastructure, but the benefits could be huge. For example, a smart port system can reduce cargo and vehicle handling time from 15 hours to 2.5 hours, and decrease the number of documents submitted by an agent from 53 to 11. Now with limited resources and capacity to expand physical infrastructure, the focus should be on leveraging ICT technology to unleash the full potential of the existing infrastructure. Government agencies should take an incremental approach and demonstrate benefits through small projects. This helps to educate both the workforce and the public and improves the feasibility from the funding perspective. Transformative Technologies in Transportation39 Leveraging the Opportunities Offered by Private-Public Partnership Traditionally, roads have been provided as a public good, with free access to all, making the introduction of pricing schemes a challenge due to the potential resistance from public. However, infrastructure development under private-public partnership (P3) offers greater flexibility with innovative finance models and management strategies. These include congestion pricing and active travel demand management schemes, which can become an integrated component in the smart systems (for example, smart mobility system and equitable pricing system discussed above). The World Bank is assisting Uzbekistan in building the country’s first ever privately financed toll road. Moreover, P3 projects shift some project risks from public-sector owners to private-sector concessionaires via long-term contracts and create a mechanism and incentives for both parties to introduce innovation. Governments can also leverage the expertise and resources from the private sector, introduce innovative financing mechanisms, and navigate the challenges associated with implementing pricing schemes. It enables the realization of smarter and more sustainable infrastructure systems while addressing the need for equitable pricing solutions. Empowering Smart Infrastructure Through Collaborative Data Policies Data play a vital role in the sensing-controlling-actuating model that powers smart infrastructure systems. Effective policy making is crucial to develop a cohesive, secure, privacy-centric, and ethical data-sharing ecosystem that can unlock the associated benefits. Fragmented data solutions, on the other hand, risk leaving significant economic, social, and environmental value untapped. The sustainable mobility for all, a platform for international cooperation on transportation and mobility issues hosted by the World Bank, proposed a five-layer data sharing policy framework: 1. Data collection and merging 2. Data standards 3. Data infrastructure 4. Governance and accountability 5. Use and analysis This could serve as a starting point for fostering policy dialogues among stakeholders. Government agencies should play different roles in various aspects of data based on their strengths and weaknesses within their respective departments. For example, transit agencies are best suited to generate transit network and service data as they are the primary service providers in most cities. However, they may lack expertise in maintaining and distributing such information effectively. Conversely, companies like Google have created the GTFS, a standardized format for public transit networks and schedules. Private travel information providers have also developed versatile applications that greatly enhance users’ travel experiences with transit. To foster effective data policy, it is essential for government agencies, private sector entities, and other stakeholders to collaborate and leverage their respective expertise. Government agencies can contribute by establishing guidelines and regulations, ensuring data security and privacy, and facilitating interagency coordination. The private sector can bring innovation and technical expertise to create data-sharing platforms and develop user-friendly applications. By harnessing the strengths of different stakeholders and promoting a culture of collaboration, governments can take a leading role in shaping data policies that enable the realization of the full potential of smart infrastructure. Transformative Technologies in Transportation40 Embracing the Power of AI and Staying Open-Minded AI has become an integral component of smart infrastructure and smart mobility. It forms the foundation of autonomous vehicle sensing and navigation capabilities and offers vast potential for diverse applications in the policy making, planning, and operation of transportation systems. It can be used to help developing countries provide safer, more efficient, and more environmentally friendly transportation services with less investment, and thus, positively impact their whole economy. As we embark on the journey toward an AI-driven future, it is crucial to foster open-mindedness and embrace the transformative potential of emerging technologies. AI has the capacity to revolutionize transportation systems, and by staying receptive to innovation, its capabilities to drive sustainable and inclusive development can be leveraged. By actively embracing AI and maintaining a willingness to adapt, governments, businesses, and the society can together navigate the evolving landscape of smart infrastructure and unlock its immense benefits. However, quantifying the full extent of benefits associated with smart infrastructure can be challenging, as they extend beyond mere improvements in mobility. These benefits include social inclusion, safety enhancements, economic advantages, and environmental considerations, among others. To effectively communicate and showcase these benefits to stakeholders, further pilot projects and empirical studies are necessary. In addition, smart infrastructure helps to pave the way for innovations in the private sector. Chapters 3 and 4 highlight examples of passenger and freight traffic, respectively. References Al-Qadi, I. L., A. Loulizi, M. Elseifi, and S. Lahouar. 2004. “The Virginia Smart Road: The Impact of Pavement Instrumentation on Understanding Pavement Performance.” Journal of the Association of Asphalt Paving Technologists 73 (3): 427–65. Anderson, P., M. J. Walk, and C. Simek. 2020. Transit Signal Priority: Current State of the Practice. 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World Economic Forum. https://www.weforum.org/agenda/2019/06/what-africa-can-learn-from-china- about-data-privacy/. 3 Emerging Passenger Mobility Trend - Connected, Autonomous, Shared, and Electric (CASE) Discover the transformative power of Connectivity, Automation, Shared Mobility, and Electrification (CASE) strategies. The implications of CASE technology extend to multiple interdependent levels: the supply of mobility services; demand and behavioral changes; system operational performance, and use of alternative energy sources. Electrification • An eco-conscious Shared Mobility initiative that results in sustainable public Automation • Car sharing transportation • Shared micromobility • Reduced maintenance • Enable self-driving capabilities (Bike, Scooter, and Moped) and operation costs • Enhance safety and flow strategies • Shared Ride and Connectivity • Complement • Improve quality of life with delivery services sustainability initiatives • Enable real-time navigation mobility tools and robotic assistants (app-based ride-hailing) that help achieve and route information • Improve stability and reliability of • Shared informal ride zero-emission energy • Allow remote activities and travel times • Super apps goals by opting for dynamic scheduling • Personalize service in lower-density alternatives such as areas and focus on frequent rapid FCEVs, sustainable • Target aspects of urban aviation fuel, biofuels, transportation, including service in higher-density corridors hydrogen, ammonia, and bus tracking, ride-hailing, • Offer on-demand air mobility and synthetic carbon-based and parking spot location goods delivery in urban, suburban, fuels through apps and rural communities through • Analyze joint travel patterns drones • Serve as traffic probe CASE C A S E • Reduced waiting times for transit services • Seamless access to airports and major • Autonomous vehicles with levels of self-driving capabilities • Drones for increased • Includes car-sharing, shared micro-mobility (two-wheelers), and shared informal rides • Motivated by interest in decarbonizing the transport sector • Strong economic and terminals accessibility and reach • One-stop platform with sustainable reasons to to rural areas personalization, opt for EVs • Challenges in data simplifies mobility privacy and regulatory • Challenges in market • Challenges in policy, concerns adoption and • Challenges of safety and economic, and financial implementation reliability realities Policy Implications Leverage the Opportunities Brought by CASE to Advance Future Mobility Promoting Social Equity and Addressing Disparities in Access to Mobility Enhancing Mobility Services Provision Through CASE Deployments Proactive Policy Planning to Address Labor Market Impacts From CASE Transformative Technologies in Transportation46 Chapter at a Glance In both developed and developing economies, a convergence of technological, economic, mobility, social, and demographic trends is poised to revolutionize passenger mobility services. This chapter aims to review and analyze the latest developments in emerging technologies within the passenger mobility sector, which have the potential to bring about transformative changes. It will explore the implications of these technologies on various aspects, including travel patterns, economic activities, individual and social well-being, and the environment. These technologies mainly fall under the four categories presented by connectivity, automation, shared mobility, and electrification, collectively referred to as CASE strategies. Within each category, the recent advancements are examined and insightful vignettes that illustrate their potential impacts are presented. The picture that emerges is that the provision of more of the same types of existing infrastructure would not be the most effective investment for urban mobility futures; the mobility infrastructure must be re-conceived and reinvented to better serve the users and public sectors need to be better prepared and shape the trend. Transformative Technologies in Transportation47 Context In a 1988 National Academy of Engineering study titled “Cities and Their Vital Systems: Infrastructure Past, Present, and Future,” Ausubel and Herman (1988) state: “Cities are the summation and densest expression of infrastructure, or more accurately, a set of infrastructures, working sometimes in harmony, sometimes with frustrating discord, to provide us with shelter, contact, energy, water, and means to meet other human needs. The infrastructure is a reflection of our social and historical evolution. It is a symbol of what we are collectively, and its forms and functions sharpen our understanding of the similarities and differences among regions, groups, and cultures.” They go on to define the physical infrastructure as consisting of various structures, buildings, pipes, roads, rails, bridges, tunnels, and wires. Even in 1988, they recognized that equally important and subject to change is the “software” for the physical infrastructure, all the formal and informal rules for operation of the systems — which anticipated the present era of intelligent connected mobility systems, and the associated vision for smart cities. The distinction between the physical infrastructure and how it is operated and managed is essential to understand how cities and mobility can evolve through the influence of technology to meet changing economic requirements and societal expectations. It is particularly relevant in the context of developing countries, which vary widely in terms of: • The extent, coverage, and condition of their physical infrastructure networks • Degree of organization and operational efficiency • Governance structures and institutional capacity • Financial adequacy and stability • The changing nature of the demands placed upon them Accordingly, emerging technologies (especially in the realm of digitalization) that are less dependent on legacy, heavy infrastructures may enable these cities to leapfrog and reconfigure mobility and related services to better match their needs, aspirations, and capabilities. The discussion of the mobility challenges and opportunities to leapfrog is timely because of the real challenges that cities across the world are facing, particularly in the developing world. Between 2015 and 2050, the world population is expected to increase by nearly 2.5 billion, with an estimated 97 percent of this increase occurring in developing countries (Walker, 2016). According to UN estimates, by 2050, 68 percent of the world’s population is expected to live in cities (UN, 2018). Rising household wealth in many parts of the world is also contributing to increased motorization rates. Both rapid urbanization and motorization have the potential to contribute to climate change, while making more people vulnerable to its impacts, such as natural disasters, severe weather events, droughts, and famine (UN, 2017). Reducing vehicle ownership and use, increasing vehicle occupancy, and substituting trips with digital services (for example, telework/work-from-home and telehealth) represent key strategies that could help control and reverse the growth of vehicular GHG emissions. In cities around the world, innovative and emerging mobility strategies are offering consumers more options to access mobility, goods, and services. These innovative and emerging mobility strategies are also disrupting labor markets and, in some cases, creating opportunities for employment. In recent years, economic, environmental, and social forces started contributing to the growth of shared mobility in both developed and developing countries. Shared mobility — the shared use of a vehicle, motorcycle, auto rickshaw, minibus, scooter, bicycle, or other travel mode — is an innovative Transformative Technologies in Transportation48 transportation strategy that enables users to have short-term access to a transportation mode on an as-needed basis. In the coming decades, the convergence of a variety of transportation technologies, such as sharing, automation, and electrification has the potential to change how people travel and access goods and services. However, early evidence suggests that innovative and emerging mobility technologies could have mixed impacts on a variety of social, environmental, equity, and labor outcomes. As these services grow in many regions of the world, consumers are engaging in more complex multimodal decision-making processes. On the demand side, rather than making decisions between modes, travelers are linking modes to optimize route, travel time, and cost. Additionally, fare and digital information integration has the potential to enhance consumer convenience, increase transparency, and reduce costs. In some cases, consumers are opting for the goods and digital delivery instead of making a trip. On the supply side, innovative and emerging mobility strategies may offer new and flexible employment opportunities. However, the impacts of these strategies on incumbent services, particularly in developing countries, are not well documented. Moreover, the impacts of highly automated vehicles on labor may result in uncertain structural changes in both the number of jobs and skills required to meet workforce requirements. To address these concerns, targeted policy intervention based on a deep understanding of technological innovations and local conditions may be needed. Powered by the increasingly connected infrastructure, the digitization and seamless connectivity is innovating all transportation sectors: private, public, shared, and informal. Through connected infrastructure and mobility services, travelers who are attracted to and reliant on real-time mobility data, app-based booking and payment, and other digital services are also connected and contribute to the system through data sharing. CAVs (see Box 3.1) and advanced air mobility (AAM) technologies are moving from labs to the field, with profound impact on the entire mobility ecosystem. Alternative energy technologies, led by the huge improvement in battery technology, are transforming personal mobility to a greener future. While some developments in each of these areas are independent and have their own dynamic, several are interdependent and synergistic, which paints an exciting picture of future mobility. They could also potentially impact important social issues such as equity and labor market, where decisionmakers could better prepare themselves and shape the trend. The issues are complex, as they involve the quality of life, social, economic, institutional, and fiscal considerations as well as the technological and functional aspects. Failing to grapple with these technological opportunities may not only entail a significant opportunity cost, but may otherwise see them evolve in a counterproductive manner providing motivation for public entities to seek to “shape them before they shape you”. Given the rapid rate at which several relevant technologies are evolving, any attempt to define a single future point in time is likely to miss the mark in some respect. Hence, this report will view the future as a blend of various factors and trends, the origins of most of which are apparent, while the others are possibly still unknown. Rather than formulating mutually exclusive scenarios, the report will look at a range of possibilities and seek to identify infrastructure implications and policy enablers that may favor or preclude some of these possibilities, with particular emphasis on those that point in directions that society may broadly consider more desirable. Transformative Technologies in Transportation49 Technology Trends The technologies discussed in this section have been enabled by more fundamental developments in sensing, communication, and computing (information) technologies. The intent is not to discuss the basic technologies per se, but to examine their application in transportation systems and/or their impact on the travel and activity behavior of individuals. The four main trends discussed are: connectivity through personal mobile communication devices and telemobility, autonomous vehicles, shared mobility, electrification and alternative energies. These technologies have taken place in different industry sectors to address different applications and market motivations. . Connectivity Through Personal Communication Devices and Telemobility Taken for granted in most of the world, mobile phones have probably been one of the most impactful personal technologies of the past 20 years. With more than seven billion smartphone users worldwide, smartphones have become near ubiquitous around the world. Coupled with wireless access to the internet, GPS location, and audio and high-definition video processing capabilities, handsets have morphed into smartphones with the computing power of high-end workstations, enabling essentially continuous anytime/anywhere access to a growing realm of virtual opportunities. Impacts of smartphones are seen across the spectrum of human and social interaction, enabling a seemingly endless stream of work; personal commercial and financial transactions; and social and recreational activities. Transportation and travel are no exception to the realm of activities supported and enhanced by smartphones and similar connectivity devices. Traveler information systems, which deliver real- time navigation and route information to travelers, have seen a major shift from a vehicle-based functionality to a personal-based application delivered via GPS-equipped mobile devices. At the same time, traveler information delivery has evolved from being a service provided by public agencies that operate the infrastructure to their users, or by vehicle manufacturers to drivers. It is now becoming a service offered by third-party providers that compete for users’ loyalty by adding value through crowdsourcing, prediction, and improved path-finding algorithms to deliver real-time information directly via smartphones. That development came about largely due to the general reluctance of public agencies to provide the full value of real-time information to users, or guidance based on that information. Services such as Waze, that rely on crowdsourced information and reports to provide real-time route information, are hugely successful with the traveling public in places where they are available. Companies providing such services also seek to leverage the loyalty of users by offering location-based services, including promotional offers (e.g. discounts on products and services), targeted at users’ specific location. Two major trends in information supply can be observed, with direct applicability to how individual travelers interact with the transportation infrastructure and related services in an urban context (Mahmassani, 2011): personalization and socialization. The former provides individualized information that considers the user’s current location, preferences (as expressed through previous choices or responses to stated choice queries) and is therefore more directly relevant to user needs. The latter shares information about one’s activities with a social network of friends and this information reflects the experience of socially connected individuals, which influence, for example, their destinations visited, and the set of considered alternatives (Chen, Mahmassani, and Frei, 2018). A development worth watching is the increased reliance on smartphone apps to influence behavior through gamification (feedback, keeping score, milestones, rewards) and personalized incentives. Areas of intervention include getting the user to make choices that are better for the environment or their health, or reducing the traffic congestion and unreliability experienced by the user. Transformative Technologies in Transportation50 In terms of the demand for travel, mobile internet access has further enabled and helped expand an important phenomenon that started with fixed internet access in the 1980s and 1990s, namely the ability to conduct remote activities that otherwise required physical presence. These include work (telecommuting), shopping, and transactions ranging from financial and legal to passport applications and payment for traffic fines. Increasingly, there has been a growing convergence of individuals’ physical and virtual worlds, and mobile broadband access via smartphones has been an important factor in this process. Such telemobility may entail changes in the nature and spatial characteristics of the activities conducted, along with their social dimensions, rendering the process of activity generation (that is, formation of potential activity choice sets) and scheduling considerably more dynamic and real-time in nature. As such, it could enable expanded access to those who may otherwise be disadvantaged locationally or are mobility impaired, for example, residents of remote villages can receive higher quality of medical care, provided they have access to the communication technologies and the knowledge to use them. The recent experience with the COVID-19 pandemic and the policy measures intended to contain it have created a unique, large-scale natural experiment in substituting virtual engagement for physical in-person interactions across most domains of human activity, including telework, e-commerce, e-learning, telemedicine, and e-health, as well as e-sports and e-entertainment. Researchers and planners are yet to fully realize the extent and depth of these impacts, though initial findings from different parts of the world have started to appear in the literature (for example, Hensher, Beck, and Wei, 2021; Mouratidis and Papagiannakis, 2021). Directly in urban mobility, the past 10 years have seen an explosion of specialized smartphone apps targeting some aspects of urban transportation, from multimodal traveler information to mobility service procurement (Shaheen et al., 2016). Bus trackers, which rely on GPS information on bus locations, provide travelers with estimated arrival times of buses at a given stop. Real-time ride- hailing applications such as Uber and Lyft provide a complete platform for ordering and purchasing rides. The ability to track the location of one’s driver is often claimed as a key benefit of the system, as is the efficient manner of transacting payments. Parking spot location and reservation apps are emerging in several urban markets around the world, for both public on-street parking as well as privately operated garages. The ability to match demand with supply in real time, while providing a convenient mechanism for completing the transaction, as well as a platform for tracking and rating one’s experience, is disrupting conventional services (such as the taxicab industry) as much as it has in areas such as visitor accommodations and trucking (Rayle et al., 2014). An equally important opportunity lies in the fact that smartphones de facto turn every individual traveler into a potential traffic probe. Beyond travel time information on different portions of the network, which several companies have begun to leverage commercially, smartphones could provide information on choices higher up the hierarchy (for example, destination choice). In addition to real-time applications for improved state estimation, such information greatly augments the data available to support planning studies and processes. This capability of smartphones is especially important in environments, such as in many developing countries, where regular survey-taking practices are not well established. Surveys are generally time-consuming, costly, and prone to representation biases associated with the systemic difficulty of reaching certain segments of the population. Hence, the ability to leapfrog such practices to rely more extensively on passively gathered information through cell phones and other devices represents an important opportunity with significant potential payoff. Transformative Technologies in Transportation51 There is widespread recognition that new technologies are continuing to enable new ways to measure and track individual choices (Mahmassani et al., 2014). This goes a long way toward observing actual choices of modes, routes traveled, destinations visited, and so on. Coupled with social networking information, planners can also analyze spatial and temporal patterns of joint travel choices by related individuals. While concerns for privacy will continue to remain paramount, considerable useful information for transportation planning and analysis can be obtained while maintaining the anonymity of the individual travelers. The transportation domain is seriously lagging other domains (for example, online and retail marketing) when it comes to mining and leveraging the vast amount of data that are accumulating through nontraditional sources such as smartphones, internet transactions, and video images of the transportation system itself (for example, at train stations and on many highways and intersections). Some of this reluctance on the part of the public sector is due to the sheer novelty of these data streams, as well as concerns around data privacy and data representativeness. With the recent emergence of several aggregators and data vendors in the sector (for example, INRIX, Cuebiq, Google), more of these data are finding their way into agency practice, although mostly for performance monitoring, and in some instances, for the validation of the operational performance aspects (travel times, speeds) of large network simulation models. The prevalence and availability of such data sources varies widely across cities of the world, reflecting both different regulatory schemes that may limit the ability of the private sector to either collect or provide such data. This remains a significant opportunity to leapfrog technologies in data collection, spatial pattern characterization, and foundational data for planning processes in many cities of the developing world. Box 3.1. Connected Vehicle System CV technology is expected to improve safety by enhancing drivers’ situational awareness while reducing congestion, energy consumption, and the negative environmental effects of driving. It provides the opportunity to create an interconnected network of moving vehicular units and stationary infrastructure units, in which individual vehicles can communicate with other vehicles (that is, vehicle-to-vehicle or V2V communication) and other agents (for example, a centralized traffic management center through vehicle-to-infrastructure, or V2I communication) in a collaborative and meaningful manner. The real-time information provided by V2V and V2I improves drivers’ situational awareness and enhances the safety and efficiency of operating vehicles. It also improves the reliability of the traffic system through online monitoring and dynamic management, while providing data for both online operations management and offline planning applications. From a traffic operations perspective, a key focus of CV systems is to enable coordinated strategies that improve the quality of flow along highways and at intersections, including speed harmonization, coordinated cruise control, and queue warning (Mahmassani et al., 2012). In general, the more vehicles are connected, the greater the opportunity for coordinated interventions to improve the quality and reliability of flow. In an urban setting, CV technology enables more responsive operation of traffic controls (especially traffic signals) and more efficient sharing of right of way by different types of vehicles, including transit vehicles along priority corridors. Connectivity will also enable more effective demand management by integrating information to and from travelers into the overall system and improving the user experience and multimodal mobility. Transformative Technologies in Transportation52 Beyond the immediate scope of connecting transportation vehicle and infrastructure systems, technology companies have put forward the notion of an internet of things (IoT) in which machines, objects, people, and vehicles of all types are interconnected. For an individual, the typical image envisions their home, office, and vehicle all interconnected, placing the user at the center of a web of seamless connectivity, where physical and virtual worlds become a continuum of activity engagement. Connected cities with shared data platforms and intelligent processes that leverage the data offer opportunities for end users (city dwellers, travelers), system operators, and managers, as well as third parties to offer plenty of potential services (see Box 3.2). For individual users, the value proposition translates into greater user convenience and seamless telemobility, referred to as the “connected life”. For system operators, the opportunity is of greater efficiencies while delivering better service to consumers, through the application of advanced predictive analytics and intelligent control. The availability of large data streams from various public and private sources, with the ability to reach consumers near-instantly through connected mobile devices, creates many new opportunities for entrepreneurial third parties to improve existing services or offer entirely new categories of services and experiences. Box 3.2. Internet of Things and Connected Cities Early forms of this vision were articulated nearly 30 years ago. In 1991, Mark Weiser, then of Xerox PARC, coined the term Ubiquitous Computing to describe “a world in which objects of all kinds could sense, communicate, analyze, and act or react to people and other machines autonomously” (Holdowsky et al., 2015). The value proposition is quite simple; by connecting more devices, the IoT enables a more complete view of interacting machines, devices, and systems, thereby enabling better prediction and more effective interventions, as well as entirely new views and the opportunity for a wider range of interventions. The kind of data and systems integration envisioned under an IoT, when applied at the level of an urban area, results in smart cities, where a web of connected sensors of all types, along with shared data platforms, enables efficiencies across urban services in different sector, (for example, education, health care, electric power, and water), in addition to mobility services. The concept of smart cities has been around for at least the past decade, reflecting a natural evolution and adoption of ICT technologies in urban services (Dirks and Keeling, 2009; Batty et al., 2012). In terms of personal urban mobility, the quality, scope, and relevance of real-time information would contribute to reducing waiting times for transit services, enable the reservation and payment for parking spots at congested locations, simplify access across a spectrum of urban modes such as shared bikes and vehicle fleets, facilitate seamless access to airports and major terminals, and so on. For users, this means greater convenience; for cities and operators, greater efficiencies and better utilization of resources; and for society, more livable and environmentally sustainable cities. Transformative Technologies in Transportation53 Box 3.2. Internet of Things and Connected Cities (Cont.) From the perspective of urban scenarios for 2050, while the basic IoT technologies are mostly in place, there are several questions about the process by which their adoption and deployment into smart cities might unfold, and how this process may influence the resulting configuration: • System architecture: Will a dominant IoT platform emerge, applicable across different domains (hence dramatically increasing opportunity)? Given the scale and scope of such an endeavor, it is more likely that smaller-scale, smaller-impact, independent projects will first emerge, building on and upgrading legacy systems, with greater integration taking place over a longer time frame. • Who leads (now) or will lead in this process? While there are several industry players striving for the distinction, no particular industry sector or company has emerged in a clear dominant role. Will it be the device side (manufacturers of devices), system integrators on the data side, or end application developers? What might be the role of public policy and public choice in this process? • How open should the IoT data platform be in enabling a smart city? Greater openness and access encourage innovation and entrepreneurial risk taking, and thereby the emergence of new services, but it may need to be tempered due to concerns of privacy violations and commercial interests. • Smart cities also have implications for governance; urban planners in particular have cautioned that it takes more than technology to create smart cities. It also takes people, communities and institutional change, and an active program for community engagement (Campbell, 2013; Goodspeed, 2015). In summary, connectivity and IoT increase opportunity for users, the overall system, and third parties. The more “things” that are connected, the more sectors integrated within a city (sources of data), the greater potential. Transportation and mobility industries are likely undergoing major disruptive influences in terms of technology, players, and concepts. One of the substantial hurdles for achieving the kind of integration envisioned under smarter urban systems is expected from the public sector, which controls large sensor data sets and has a mandate to operate several critical infrastructures and services. Smart cities entail levels of intra- and inter-agency coordination and process redesign that may be more difficult to accomplish in certain cities than in others. Hence, there are likely to be different degrees of adoption, and different models of public-private engagement to deliver the potential benefits of urban-scale connectivity. Autonomous Vehicles Autonomous cars The popular media have been replete with images of autonomous, or driverless, cars over the past few years, especially the “Google car”, which marked the technology giant’s well-publicized entry into the vehicular realm. The vision is certainly not new, and examples of images of cars driving themselves while the occupants engage in work or recreational activities can be traced far back. However, advances in computing, robotics, and AI have enabled the realization of near road-worthy vehicles, prompting serious efforts at the regulatory, legal, and insurance levels to facilitate the entry of such vehicles into daily use. Transformative Technologies in Transportation54 Figure 3.1. Society of Automotive Engineers’ Levels of Automation 0 No Automation Zero autonomy; the driver performs all driving tasks. 1 Driver Assistance Vehicle is controlled by the driver; but some driving assist features may be included in the vehicle design. 2 Partial Automation Vehicle has combined automated functions, like acceleration and steering, but the driver must remain engaged with the driving task and monitor the environment at all times. 3 Conditional Automation Driver is a necessity, but is not required tp monitor the environment. The driver must be ready to take control of the vehicle at all times with notice. 4 High Automation The vehicle is capable of performing all driving functions under certain conditions. The driver may have the option to control the vehicle. 5 Full Automation The vehicle is capable of performing all driving functions under all conditions. The driver may have the option to control the vehicle. Source: SAE International, 2021. A useful framework for thinking about AV capabilities was articulated by the Society of Automotive Engineers (SAE). SAE defines five different levels of progressive automation, relative to a base level (Level 0) of no automation, as shown in figure 3.1, with Levels 4 and 5 corresponding to full self-driving automation, under which the responsibility for safe operation rests entirely with the automated system, hence allowing the vehicle to also operate while unoccupied. Compared with Level 5, Level 4 precludes autonomous operation from designated areas where the infrastructure may not be suitable, or the presence of complex high-density interaction between pedestrians and other vehicles may pose challenges for the self-driving logic. Levels 1 and 2 can be viewed as driver-assistance functions, and are already essentially available in standard higher-end vehicles. Transformative Technologies in Transportation55 These levels translate primarily into marginally greater levels of safety and convenience for the vehicle occupant, with essentially no substantial impact on the overall traffic and mobility systems. Level 3 begins to introduce substantial “driverless” capabilities, while still requiring the participation of a human driver, in what is likely an interim stage before full self-driving capability. AV capabilities are often discussed in conjunction with CV systems. The two are, in effect, distinct. Autonomy is envisioned by the likes of Google as the ability to drive with no external assistance, possible through extensive sensing and massive intelligence residing fully within the vehicle. All these functions could be enhanced through connectivity, for example, when neighboring vehicles and or the infrastructure convey messages to other vehicles about respective locations, road features, or control displays. Additional coordinated strategies could thus be enabled to further enhance safety and flow quality. However, in this case, more of the intelligence resides in the infrastructure, or the vehicle-infrastructure system, instead of residing exclusively in individual vehicles. All these factors have important implications for deployment, coordination, vulnerability, and resilience of the associated system design and deployment scenario. Most notably, CV systems require a much greater degree of coordination among auto manufacturers and traffic management authorities, whereas Avs are envisioned as fully self-sufficient, given the existing physical infrastructure. Three distinct, but inter-related, aspects of AVs are of particular importance when answering questions regarding urban mobility and its implications for the infrastructure: 1. Extent and pace of market adoption 2. System-level impacts on flow quality and capacity 3. New models for mobility service delivery The first two are briefly discussed hereafter, while the third is the subject of the next section under shared mobility. Market adoption. A major determinant of the impact of Avs on traffic flow and urban mobility will be the extent to which these vehicles are adopted and accepted by users, and the fraction of the total vehicle mix that they constitute. This will naturally depend on when they are introduced commercially, how they are marketed, what restrictions, if any, are placed on their use, and the price at which they are offered. It will also depend on the manner through which their use is made available to the public; greater availability through shared fleets might mean less need to actually own the AV, which could be ordered when needed — “buying mobility by the drink instead of by the bottle” (Kornhauser, 2014). Whether in shared fleet use or individually owned, adoption by users will likely depend on four key factors (Mahmassani, 2014): 1. Trust 2. Ability to drive 3. Benefit perception, in terms of safety, mobility, and efficiency — time saving, activity constraint reduction 4. Affordability Going beyond the trust issue and assuming legal and institutional matters are satisfactorily resolved to allow at least comparable levels of peace of mind as with current automobiles, potential users’ perception of the technology’s benefits is especially important, as it also determines the manner Transformative Technologies in Transportation56 in which the vehicles might be used in fulfilling individuals and households’ activity patterns. This, in turn, would determine the extent of vehicle-miles traveled, resources consumed, carbon impact, and other externalities. The benefits derive from two functions of the AV: (1) as a mobility tool, it is expected to provide greater safety, as well as efficiency, enabling multitasking especially over longer spans of travel; and (2) as a robotic assistant, it could now go shop, pick up kids, and perform similar mobility chores imposed by an auto-centric suburban lifestyle. For small businesses, the autonomous car could deliver and pick up supplies. As such, autonomous cars may save its owners money and time, enabling uses for activities previously either not done, postponed, or chained, along with possibly reorganizing activity patterns, especially for caregivers (of young people, elderly). Authoritative projections of market adoption are not yet available, especially in the absence of an official timeline of commercial availability. Whether cities will be fully ready to accommodate them, and to take full advantage of the benefits that they may provide, is another matter. Some cities around the world are clearly ahead of others (for example, Dubai, Singapore); most, however, have not gone through the planning exercises necessary to understand the full implications of AV introduction and adoption. They have also not formulated adequate strategies and plans to advance their public infrastructure, management structures, or related services to not only accommodate but realize the potential social benefits of improved mobility through automated and connected technologies. Flow quality and capacity. Several studies of both autonomous and connected vehicles conducted in the 1990s (Shladover et al., 1991; Varaiya, 1993; Godbole et al., 1995) and more recently (Varotto et al., 2015; Talebpour, 2015) investigate the flow properties of vehicular traffic streams with varying fractions of Avs and/or CVs. These properties will be determined by the specific technologies and how they are implemented. For example, the specific logic by which a driverless car would follow other vehicles, change lanes, and so on; the sensors used and the pattern recognition algorithms; and the interaction protocols for vehicles with different levels and types of technologies. Investigations to date suggest meaningful improvements in most flow performance indicators. The four main performance indicators considered are safety, capacity, stability, and reliability. The potential of eliminating one of the main causes of crashes, namely driver limitations in cognition, time lags, and response is a substantial safety benefit. Capacity benefits derive in principle from the ability of AVs/CVs to follow one another more closely while maintaining higher speeds than is the case with manually driven vehicles; the latter require more separation at higher speeds to allow driver-vehicles to safely react to sudden deceleration of vehicles ahead. Higher throughput can be expected when vehicles maintain higher speeds at higher densities. Theoretically, all- AVs with no lane changes would result in throughput gains as high as four or even five times of presently observed capacities. More realistic estimates would place an upper bound of about twice the capacity gain, with a 30 to 50 percent increase to be more conservative for even high market penetration rates. More important than the maximum possible flow rate is the potential for sustaining higher throughput levels without the occurrence of flow breakdown (“capacity drop”) during peak periods, as is currently the case in most congested cities. The introduction of AVs is expected to increase the stability of flow. This increase in stability has been established theoretically as well as through simulation, when even a small fraction of AVs is introduced in the traffic mix (Talebpour and Mahmassani, 2016). The reduction of the likelihood of flow breakdown in turn results in more reliable travel times. Transformative Technologies in Transportation57 Impact of AVs. The emergence and adoption of AVs is expected to accelerate certain trends in shared urban mobility and result in new hybrid forms of mobility service that bridge traditional transit service and personal mobility. These emerging service concepts further leverage the kind of personal mobile technologies discussed above, enabling greater personalization of not only information but also the services delivered. These potential changes in the supply of transportation and mobility at the urban scale are difficult to predict and characterize for the purpose of developing specific planning tools and forecasting the demand for these services over time. The following aspects can be noted based on the current understanding of travel behavior and the expected features of AVs. • Driverless vehicles will enable new forms of mobility supply. By eliminating the cost and performance limitations of human drivers and increasing the ease of communicating instructions to both vehicles and travelers, AV fleets can be operated efficiently to deliver dynamically scheduled services to individuals riding privately or in shared vehicles. • New forms of carsharing with greater convenience may reduce the motivation for individual ownership. With driverless cars, availability of a vehicle in sharing services is not limited to the nearest lot. Vehicles can be repositioned dynamically to the user’s location from anywhere. • The market for ride sharing and carsharing will likely expand with the emergence of driverless vehicles, with platforms developed by ride-hailing app companies like Uber and Lyft taking the lead. Increasing the supply pool and enabling rapid dispatch of driverless vehicles would contribute to reducing the cost and uncertainty of the sharing model. • With transit companies adopting a broader portfolio of services, possibly in conjunction with third parties, conventional fixed-route, fixed-schedule bus services in lower-density communities will be supplanted by driverless, personalized services at low densities and shared hybrid forms at medium densities. Higher-density travel corridors will see greater focus on frequent rapid services along dedicated right of way (rail and/or BRT), made more efficient and accessible via driverless hybrid options. With AVs, greater dispatching flexibility is possible, enabling the equivalent of personalized services at times and shared rides at others, depending on the specific demands prevailing at a certain time. Advanced Air Mobility (AAM) and Uncrewed Aircraft Systems (UAS) AAM is a broad concept that focuses on emerging aviation markets and use cases for on-demand air mobility and goods delivery in urban, suburban, and rural communities (Cohen, Shaheen, and Farrar, 2021). Airbus’ Voom operated helicopter services in Mexico City and Sao Paulo until April 2020 when the global pandemic caused an overall drop in travel demand. Voom reported 150,000 active app users, 15,000 helicopter passengers, and a 45 percent repeat customer rate. Over this pandemic period, Voom estimated an average ticket price approximately twice the cost of a private ground taxi with an average travel time savings of 90 percent (Airbus, 2021). Several countries, such as the United Arab Emirates (UAE), India (see Box 3.3), Indonesia, and Malaysia, are planning similar passenger services using novel aircraft designs (for example, vertical take-off and landing, electrification, and automation). In Africa, UAS (commonly referred to as drones) have been deployed in medical use cases since 2016 in Rwanda and 2019 in Ghana (de León, 2019; Toor, 2016). In Africa, Zipline has flown more than 1.8 million miles to airdrop medical supplies and ferry viral tests from more than 1,000 medical facilities, eliminating the need for in-person deliveries. As of the summer of 2020, Zipline’s fixed-wing drones Transformative Technologies in Transportation58 had already made 30,600 medical supply deliveries since the start of the COVID-19 pandemic. In addition to delivering medical supplies, the company transports viral test samples from remote parts of Ghana, which do not have testing facilities, to laboratories in more populated parts of the country. The Zipline service is also used to expand access to medical care for patients, including delivering cancer drugs to patients in remote villages who are unable to travel due to pandemic-related quarantines and lockdown restrictions. In areas where the road infrastructure does not support deliveries, drones have reduced the transportation time to access medical supplies and testing facilities from many hours or days on a motorbike to 15 minutes. A number of global cities are also using drones to sanitize and disinfect large public facilities, such as roadways, plazas, indoor buildings, and recreational facilities. For example, communities in China (Jilin, Shandong, and Zhejiang), Honduras (Tegucigalpa), Indonesia (Surabaya), and the UAE (Dubai) are repurposing agriculture drones originally intended for crop dusting to sanitize public spaces. In Dubai, drones have been used to sterilize 129 municipal sites and 23 public areas as part of the city’s sanitation program. The drones can fly 10 to 15 minutes on a full charge and cover approximately an acre per hour (Bourke, 2020). Supporters of this use case suggest that drones may be able to disinfect larger areas with less labor, although more testing and research are needed. Nevertheless, it is important to note that other sources question the efficacy of this practice and have pointed out the potential for negative public health outcomes and environmental pollution (WeRobotics, 2020). The COVID-19 pandemic has increased public familiarity with UAS applications (for example, certain life-saving functions); however, drones can be controversial. Privacy advocates have expressed a number of concerns including the potential for drones to (1) employ invasive surveillance technologies such as night vision, LiDAR (laser detection), infrared sensors, and other equipment to create 3D maps; (2) monitor individual behavior; (3) accumulate and share sensitive medical information (that is, temperature checks and contact tracing); and (4) collect information from mobile phones. Image 3.1. Drone to disinfect roadside during COVID-19 outbreak Source: Flickr. Transformative Technologies in Transportation59 Box 3.3. Advanced Air Mobility in India The Indian government’s Invest India program is a national economic development initiative that helps investors find investment opportunities in the country (for example, investments for manufacturing drones and air taxis). The National Skills Development Mission has established several workforce development programs to help prepare the nation’s workforce for transportation innovations such as AAM. To support these innovations, the country is also modernizing its electric grid, for example, with proposals for electric vertical and take-off and landing aircraft. Collectively, the Indian government is leveraging these planning efforts to integrate AAM into its long-range smart city programs. In particular, the Ministry of Civil Aviation has developed a Drone Policy Ecosystem Roadmap, establishing a framework for regulating UAS. The key goals of the roadmap include supporting economic development, social equity, and job creation. The roadmap recommends the establishment of drone corridors, drone ports (take-off and landing facilities for UAS), and other policies to support the growth of UAS for cargo and delivery use cases. A proposed version 2.0 would address a few emerging issues, such as autonomous flight and standards for airworthiness, maintenance, operations, and air traffic management (Ministry of Civil Aviation, 2019). In 2020, the Government released draft UAS regulations. Although concerns of social inequality and environmental impacts may be associated with AAM, proponents suggest that passenger mobility and goods delivery could present some compelling use cases to reduce travel times in highly congested megacities in developing countries. For example, the India Smart Grid Forum believes that AAM has the potential to reduce ground travel times between the Bangalore airport and Electronic City from two and a half hours to less than 15 minutes using on-demand air mobility on this 40 km trip. Shared Mobility Sharing is not a new concept, economic models and enabling technologies is making it easier for households in developing countries to share mobility resources, and in some cases, earn additional income by employing underused transportation resources. In developing countries, shared mobility includes a number of classic, innovative, and emerging services to meet the diverse travel needs of users. A World Bank study (Bianchi Alves et al., 2021) provided a comprehensive review of mobility as a service, and particularly shared mobility, in the context of LMICs. It pointed out that shared mobility in developing countries can use the public transit as the anchor mode, and integrate a wide range of mobility services to meet the travel needs. The development of ICT network, agile accommodation of informal transit, and the development of digital payments are all key enablers of shared mobility services. Broadly, these services also can be classified according to fleet sharing, ride services, and AAM. Fleet sharing includes services that provide travelers access to various types of shared vehicles or devices (bikes and scooters) for short-term use. Ride and delivery services provide travelers access to taxis, e-hail, motorcycles, auto rickshaws, pedicabs, courier network services, and other vehicle drivers or device operators. Additionally, aerial services enable consumers access to air mobility, such as air taxis, goods delivery (via drone, for instance), and emergency services by dispatching or employing AAM and enabling technologies through an integrated and connected multimodal network. Annex 1 provides a summary of existing and emerging shared transportation services. Transformative Technologies in Transportation60 In addition, this section presents a matrix for categorizing shared mobility including fleet sharing, ride and delivery services, innovative and emerging shared modes, and classic shared modes in developing countries (see Figure 3.2). Figure 3.2. Innovative/Emerging and Classic Fleet Sharing and Ride/Delivery Services Mobility: Advanced/urban air mobility Carsharing (i.e. Air taxis) Bikesharing E-hail Moped sharing Microtransit Motorcycle sharing Pooling (through a smartphone app) Scooter sharing Shared automated vehicles Shared micromobility Transportation network companies Delivery: Automated delivery services Courier network services Robotic delivery Innovative Ride and Unmanned aircraft systems (i.e. Drones) and Emerging Delivery Shared Modes Services Classic Mobility: Fleet Auto rickshaws Shared Sharing Informal transit Modes Jitneys Pedicabs Pooling (i.e. Carpooling and vanpoolig) Car rental Shuttles Taxis Delivery: Persona and bicycle couriers Source: World Bank. Carsharing Carsharing provides members paid access to a fleet of autos for short-term use on an as-needed basis, reducing the need for personal vehicles. Carsharing became popular in Europe in the mid-to late-1980s, and it quickly spread across the globe (see box 3.4). As of October 2018, roundtrip and one-way carsharing had an estimated 22 million members in Asia (including the Middle East) and 17,000 members in Africa. Carsharing has the potential to be an attractive option for vehicle access in developing countries, where auto ownership can be cost prohibitive, particularly for many low-to moderate-income households. Lower household incomes, import taxes, and high duties can make vehicle ownership particularly expensive. For example, in Brazil, approximately half of the purchase price of a vehicle comprises taxes such as import duties, value-added tax (VAT), license and registration fees, and vehicle property taxes. Peer-to-peer (P2P) carsharing allows users short-term access to privately owned vehicles where the P2P company serves as a broker facilitating rentals between vehicle owners and drivers, typically through an online platform. The P2P model can also be Transformative Technologies in Transportation61 an attractive option for vehicle owners seeking to offset the high costs of automobile ownership by renting out their vehicles when not being used by the primary household. A few business-to-consumer (B2C) carsharing services have emerged across developing countries such as South Africa, Turkey, and India. The primary differences are that most carsharing programs bill in smaller increments (for example, hourly or by the minute). Additionally, carsharing is typically a membership service that includes fuel/charging and insurance. Unlike car rental, technology-enabled carsharing may offer virtual access without the need to exchange keys or interface with a fleet manager. Finally, B2C carsharing programs may use low-cost labor to deliver and pick up vehicles, enabling one-way rentals, with the potential to enhance the convenience of and reduce the barriers to accessing carsharing. Box 3.4. Carsharing Programs Across Countries In May 2015, Africa’s first carsharing program, Locomute, was launched in South Africa, with services in Johannesburg, Pretoria, Durban, and Cape Town (Roux, 2019). The service offered roundtrip, one-way, and long-term rental options (the latter up to three months). In Istanbul, Turkey, Mobilizm, Mobicar, YOYO, and Zipcar offer a variety of rental options ranging from hourly to monthly. For example, YOYO offers annual memberships starting at TRY 69 (US$8). The service includes fuel and starts at TRY 33 (approximately US$5) per hour and TRY 0.50 (approximately US$0.06) per kilometer. In comparison, public transportation in Istanbul costs TRY 6 (US$0.38) per trip. YOYO also offers one-way service for an additional fee, as well as an optional valet service for dropping off and picking up a vehicle. In India, carsharing is normally referred to as self-drive car rental, and individuals typically gain access by joining an organization that maintains a fleet of automobiles. Unlike carsharing in other parts of the world, drivers usually have the option of booking and unlocking a vehicle on a mobile app or having a vehicle delivered by a valet. Common carsharing operators in India include Drivezy, Hayr, Myles, Ola Drive, Revv, VolerCars,9 and Zoomcar.10 Although there are some variations in the business (for example, B2C and P2P) and operational models (for example, roundtrip and one-way), these programs tend to cost approximately 70 (US$1) per hour for a roundtrip service and 5 to 7 (US$0.07 to US$0.09) per minute for a one-way service. Both service models typically include the cost of fuel, maintenance, parking, and insurance. Some programs have developed long-term subscription plans in partnerships with automakers that blend aspects of a vehicle lease and carsharing. For example, Revv11 offers a 12-month minimum subscription charges with a monthly fee, which includes vehicle registration and insurance. Subscribers have the option of extending their subscription up to 48 months, purchasing the vehicle, or returning it to Revv. Similarly, a number of service providers also offer carsharing services in countries across Latin America and Southeast Asia. As of January 2018, there were seven operational B2C carsharing programs in Brazil, Chile, and Columbia. Collectively, these programs had nearly 17,000 members sharing 363 vehicles (Shaheen and Cohen, 2020). In Southeast Asia, a few notable B2C programs include HipCar in Indonesia, GoCar in Malaysia, and Haupcar in Thailand. A number of these programs, such as HipCar and Haupcar, allow drivers to reserve motorcycles. A number of service providers also offer P2P services, such as Roadaz and Moovby in Malaysia and Drivemate in Thailand. 9 See https://volercars.com/. 10 See https://www.zoomcar.com/. 11 See https://www.revv.co.in/. Transformative Technologies in Transportation62 Shared Micromobility (Bike, Scooter, and Moped Sharing) Shared micromobility — the shared use of a bicycle, scooter, or other low-speed vehicle — is an innovative transportation strategy that enables users to have short-term access to a mode of transportation on an as needed basis. Shared micromobility includes various service models and transportation modes that meet the diverse needs of travelers, such as station-based models (a device is picked up from and returned to any station) and dockless services where devices are picked up and returned to any location. Across developing regions of the globe, shared micromobility is being deployed with similar service and operational models despite variations in infrastructure available for bikes and scooters. Given the overcrowding on public transportation in some countries, shared micromobility may be seen as a lower risk, socially-distanced alternative to transit and pooling during the pandemic recovery (see Box 3.5). Box 3.5. Shared Micromobility Across Countries Micromobility sharing services are emerging across Africa, the Middle East, India, and Southeast Asia. In Africa, shared micromobility developments have been limited compared to other developing countries due to the lack of dedicated infrastructure (Mwanza, 2018). A limited number of IT-based bike-sharing programs are operational in Kenya and Morocco (Medina Bike Marrakech, 2021; Zheng, 2018). In the Middle East, the UAE has become an epicenter of shared micromobility activity. The country has several operational bike- and scooter-sharing programs, mostly in Abu Dhabi and Dubai. Memberships typically range from AED 20 per day to AED 420 per year (US$5.50/day to US$114/year). Some services charge approximately AED 3 (US$0.81) to unlock and AED 0.59 to 1 (US$0.16 to US$0.27) per minute to use. In comparison, a standard one-zone public transportation fare in Dubai costs approximately AED 4 (US$1.09). A number of service providers also offer shared micromobility in Latin America. For example, a notable program, Grow, has operations in six countries and 23 cities. The service has 135,000 bikes and scooters, and recorded more than 10 million rides as of 2019 (Dickey, 2019; Intertraffic, 2019). In India, a number of service providers offer both station-based and dockless bike and moped sharing. Some of these services also allow users to pause a trip for a small fee, so that they can make multiple stops without being charged time while the device is not being used. These services generally cost approximately 3 to 6 (US$0.04 to US$0.08) per km, which includes fuel and a helmet. Some also charge an additional per- minute fee for about the same cost. One service, Bounce loop, offers P2P moped sharing that allows moped owners to rent their mopeds to other users for a fee. Similar services are also available in Southeast Asia. In response to the global pandemic, some of these programs have adapted their business models, allowing local restaurants to sign up for a bike or scooter to provide their own delivery services (Tuoi Tre News, 2020). Such services can also help expand employment opportunities for those willing to work as couriers. Transformative Technologies in Transportation63 Shared Ride and Delivery Services E-Hail is a broad term used to describe smartphone and other app-based services that allow the electronic hailing of auto rickshaws, taxis, minibuses, and for-hire rides by drivers using their personal vehicles for compensation. Ride services in developing countries are not new. The use of rickshaws or two-wheeled carts pulled by a person has a long history. Today, auto-rickshaws, a motorized version of the pulled or cycle rickshaw, are beginning to employ e-Hail apps in several countries around the world. These services are sometimes referred to as “Balaj” in Tanzania and Ethiopia, “TokTok” in Egypt, “Keke-Marwa” in Nigeria, “Raksha” in Sudan, and “Kekeh” in Liberia. In many of these countries, drivers can purchase the vehicles for approximately US$3,500. Drivers can be hired at a daily rate of approximately US$25 in many parts of Africa. Africa has more than 60 services offering a combination of taxis, private for-hire vehicles, auto rickshaws, and motorcycle taxi e-Hailing in 33 countries. In recent years, the digitization of these services onto app-based platforms has provided consumers with a greater network of drivers willing to provide for-hire vehicle services (see Box 3.6). On the supply side, drivers also find increased opportunities to provide rides for hire. The simplicity of an app-based platform has the potential to reduce barriers for riders to access mobility and for drivers to access employment. However, critics of e-Hail suggest that increased competition and less regulation may be contributing to downward wage pressure and labor exploitation. Proponents of e-Hail suggest that app-based platforms can reduce systemic inefficiencies by allowing algorithms to manage vehicle routing and manage supply and demand more efficiently through dynamic pricing. The app usually has several novel features including: (1) multiple payment options (including cash, credit card, bank account, and M-Pesa mobile payments); (2) taxi meter fare estimates, when a destination is entered into the app; and (3) the ability for riders to play radio stations through the smartphone app. Image 3.2. Taxi Service App Source: Adobe Stock. Transformative Technologies in Transportation64 Box 3.6. Shared Ride and Delivery Services Across Countries In Africa, a few notable services include Coursa, Gokidok, Gozem, Little, taptap, Taxify, Teliman, Uber, Roby, and ZayRide. For example, Gozem,12 which launched in 2018, offers e-Hail for taxis, motorcycle taxis, and pedicabs in Benin, Burkina Faso, Cameroon, Ivory Coast, Gabon, Mali, Senegal, and Togo. Little is an e-Hail taxi app available in Kenya, Uganda, Tanzania, and Zambia. Taxify, which launched in Africa in 2013, claimed 2.4 million active riders as of September 2018. The service was operational in Ghana, Kenya, Nigeria, South Africa, Tanzania, and Uganda. Uber operates across Africa and other developing countries. As of August 2018, Uber’s minimum passenger fares were US$1.75 in Nairobi, US$1.81 in Johannesburg, and US$1.11 in Lagos (Zollmann and Ng’weno, 2018). In comparison, a local bus fare in Lagos costs approximately US$1.05. Uber also has partnered with NileTaxi to offer an on-demand water taxi service in Egypt. Users can specify a pick-up and drop-off location in the Uber app by placing their origin and destination pin on the Nile River and selecting “Request UberBoat” (Egypt Independent, 2017). As of May 2021, Careem, a subsidiary of Uber, operates in more than 100 cities and 13 countries across North Africa and the Middle East including: Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Morocco, Pakistan, Palestine, Qatar, Saudi Arabia, and the UAE. In Saudi Arabia, women comprise 80% of Careem’s customers and the company has recruited women as part of the country’s Women to Drive Movement (The Economist, 2017). The company also operates Careem Now, a food delivery service in Bahrain, Iraq, Jordan, Pakistan, Saudi Arabia, and the UAE. In select locations, users are also able to have groceries delivered (Godinho, 2020). In India, the e-Hail market is split between Ola and Uber. Ola offers a variety of form factors, including motorcycle taxis, auto rickshaws, and vehicles (sedans and sport utility vehicles). Ola can be booked on the service’s mobile app or website. It also offers Ola Bike, a motorcycle taxi version of its service for as little as 1 per 4 km (US$0.01/km). Both Ola and Uber offer shared-ride options, known as Ola Share and UberPool, respectively. In Southeast Asia, Grab and Gojek are the primary e-Hail service providers. Gojek currently operates in India, Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam. As of May 2018, the service claimed to have more than one million drivers (Gojek Engineering, 2018). This service also offers GoShop, GoFood, and GoSend services that offer retail, food, and package delivery. In May 2021, Gojek announced its goal of making every vehicle, including motorcycles, on its platform electric by 2030 (Gilchrist, 2021). The service also announced a planned merger with Tokopedia, an Indonesian e-commerce and technology company (Choudhury, 2021). Grab, which currently operates in Cambodia, Indonesia, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, announced plans to go public in 2021 (Dillet, 2021). The service also offers GrabFood and GrabExpress, food delivery and courier delivery services, respectively. In.Singapore, the service has also launched GrabPet, with drivers trained to handle animals in their vehicles. Grab also has multinational partnerships with Careem and JapanTaxi, allowing their users to book rides on these platforms when traveling to the Middle East and Japan (Tariq, 2019). See https://gozem.co/en/. 12 Transformative Technologies in Transportation65 Shared Informal Ride In many developing countries, demand-responsive and informal transit can include a variety of form factors such as minibuses and vans. The term “informal” generally applies to services that either operate without governmental approval or are legally permissible, but government institutions have few resources to effectively regulate the transportation mode. Other common characteristics of informal transit services include services that operate at the discretion of the owner or operator without formal schedules or stops (Kumar, Zimmerman, and Arroyo-Arroyo, 2021). Due to the prevalence of informal services in certain transportation markets, some governments have attempted to formalize these services with varying degrees of regulation. For example, in Kenya and parts of Nigeria, “matatus” or informal minibuses comprise the majority of bus transportation (Jensen and Scott, 2017). However, over the years, the Kenyan Government has legalized these services and initiated efforts to more formally regulate the sector through licensing and vehicle inspections. Despite these initiatives, a number of informal groups began emerging to manage matatu routes, sometimes leading to violence and concerns about consumer safety (Mutongi, 2017; Wa Mungai and Samper, 2006). This has resulted in even more formalized regulation. As of 2017, there were more than 53,000 matatu licenses issued, although some experts estimate that up to 100,000 licensed and unlicensed vehicles may be operating (Latif Dahir, 2019). Technology applied to informal services can present a few opportunities and challenges for drivers, travelers, and public institutions. On the supply side, app-based services can help drivers join a more formalized network for seeking rides and deliveries. However, app-based services can also create downward wage pressure in incumbent sectors and exploit low-cost labor. For consumers, technology can improve trust and safety by providing travelers with key information such as driver photos, license plate numbers, vehicle descriptions, and driver ratings. However, technology can also provide a false sense of security. Even with technology-enabled services, riders (women in particular) still face abuse and violence (see Box 3.7). Technology can also make it easier for the public sector to regulate shared mobility and informal transportation by assembling individual drivers into a formal or quasi-formal network that can be held to minimum standards. Box 3.7. Gender Concerns in Public Transportation and Shared Mobility Public transportation and shared mobility can raise a number of cultural and gender safety–related concerns. In some developing nations, concerns about safety and cultural preferences have led to the development of specialized services for women travelers. These can take a variety of forms, such as the ability for women travelers to request women drivers (and vice versa), and the addition of tools in the app to report unsafe situations to the service providers and the traveler’s emergency contacts. In Mexico, Didi has a feature that allows women drivers to select only other women drivers (News 18, 2020). In India, Bikxie Pink is a specialized service that allows women travelers to request women drivers. Bikxie has unique features, such as an “SOS” button that can send an instant message to the customer’s emergency contacts. In recent years, app-based platforms have created new competition for matatus but could also increase the operational efficiency and help formalize a historically informal transit market (see Transformative Technologies in Transportation66 Box 3.8). For example, Swvl is an app that uses algorithms to operate fixed- and demand-responsive minibus services, similar to microtransit in developed countries. Swvl offers intraurban, intercity, and business services. Swvl “business” allows companies to book rides for their employees and monitor vehicle status in real time. At present, the service operates in Egypt, India, Jordan, Kenya, Pakistan, and the states of the Gulf Cooperation Council.13 It claims to be up to 70 percent less expensive than e-Hail, and it had a network of more than 200 routes in Alexandria and Cairo in 2018 (Nsehe, 2018). Other similar services include Uber Bus in Egypt and Careem Bus in Egypt and Saudi Arabia. Uber Bus trips in Cairo range between EGP 15 and EGP 50 (approximately between US$1 and US$3) depending on the trip length.14 The service also offers a business product similar to Swvl. In comparison, public transportation in Cairo starts at EGP 5 (US$0.32) depending on the number of stops.15 Box 3.8. Informal Shared Ride Services Across Countries In many developing countries, informal shared rides or “pooling” using smaller form factors is not a new concept. For example, Africa has more than two dozen app-based carpooling services. A few notable services include GoVoiturage, Partagi, Jumpin Rides, and Jekalo. GoVoiturage and Partagi are web-based and app-based platforms that offer carpool matching. Partagi allows drivers and riders to set match preferences such as gender, smoking, music, and air conditioning. Jumpin Rides in South Africa connects private vehicle owners with passengers going in a similar direction, allowing passengers to contribute to fuel costs. The service reported more than 11,000 users. Similar services provide carpooling in Benin, Côte d’Ivoire, Burkina Faso, Egypt, Guinea, Mali, Nigeria, Senegal, and Togo. Similarly, in India, several app-based carpooling services (for example, BlaBlaCar, Poolmycar, Quick Ride, and Sride) were operational as of May 2021. In some of these markets, the app only allows cash reimbursement to drivers for their costs to distinguish pooling from illegally operated taxi services. In other cases, pooling can be illegal altogether due to concerns that the informal sharing of rides may present safety risks to passengers. For example, in the UAE, pooling is generally illegal unless it is arranged using the Ministry of Transport app, known as “Darb”.15 In 2018, the Roads and Transport Authority suspended the issuance of Sharekni permits, which allowed drivers to share a ride (Shahbandari, 2018; Yousuf, 2018). In the first six months of 2018, more than 2,000 drivers were fined for illegally transporting passengers in their private vehicles in Abu Dhabi (Al Serkal, 2018). This offense carries a penalty of 24 black points. For context, a driver with more than 24 black points can have their license suspended for a period of time (PolicyBazaar, 2021). Super Apps In both developed and developing countries, trip planning apps have emerged as a platform for assisting travelers in identifying their preferred travel route and mode based on cost, environmental impact, time, and other considerations. They can also provide step-by-step assistance as users execute their trip plans. In doing so, they can be an enabler of shared mobility and informal transportation. 13 See https://swvl.com/homevv. 14 See https://www.uber.com/en-EG/blog/introducing-uber-bus-a-new-way-to-commute/. 15 See https://www.darb.ae/Carpooling/Home/index. Transformative Technologies in Transportation67 In Europe, services allowing travelers to access bundled mobility services are becoming more popular, which is commonly referred to as MaaS. In North America, consumers are assigning economic values to transportation services and making mobility decisions based on cost, travel and wait time, number of connections, convenience, and other attributes, a concept referred to as mobility-on-demand (MOD). MOD offers users access to mobility, goods, and services on demand by dispatching or using informal shared transportation services (for example, auto rickshaws), shared mobility, delivery services, and public transportation strategies through an integrated and connected multimodal network. In comparison, MaaS is an integrated mobility marketplace where travelers can access multiple transportation services over a single digital interface. Brokering travel with suppliers, repackaging, and reselling it as a bundled package is a distinguishing characteristic of MaaS. The public and private sectors increasingly emphasize concepts of integrated mobility. A growing number of digital services are offering connected travelers with real-time information and integrated payment services that can simplify trip planning and payment for multiple transportation modes. This helps travelers: • Search routes, schedules, near-term arrival predictions, and connections • Compare travel times, connection information, distance, and costs across multiple routes and transportation modes • Access real-time travel information for a journey, all typically from a smartphone application Although still relatively limited, a growing number of app-based services that offer digital integration are offering integrated fare payment, trip planning, and other services in developed and developing countries (see Box 3.9). For example, in Germany, Jelbi offers a single trip planning and fare payment platform with access to multiple shared modes including public transport. In Sweden, UbiGo is a service that offers households a mobility subscription in place of vehicle ownership. The subscription allows households to prepurchase mobility access in a variety of increments on multiple modes, operating like a multimodal “digital punch card” for a number of transportation services, such as public transportation, carsharing, rental cars, and taxis. In India, Kochi One integrates auto rickshaws, micromobility, and formal and informal public transportation services onto a single trip planning and fare payment platform. App-based platforms such as MaaS, MOD, and super apps can present both challenges and opportunities. The growing reliance on digital platforms and banking relationships can raise social equity concerns. The requirements for users to have smartphone and high-speed data packages to access services can represent barriers to low-income and rural households that may not be able to afford or lack data coverage to access app-based mobility platforms. Similarly, many of these app-based services may require debit/credit cards for payment and, in some cases, collateral for vehicles or equipment. They can be barriers for consumers who are underbanked or unbanked. Alternatives such as cash payment options, digital kiosks, telephone services, and nontech access (such as street hail) may help overcome some of these challenges. Despite these limitations, they present opportunities to leverage these technologies to influence more sustainable travel behavior. For example, the use of game elements and incentives present opportunities to encourage more sustainable travel behavior such as shared micromobility and public transportation use. These platforms could also be leveraged to encourage pooling and travel during off-peak periods. Thus, app-based platforms have the potential to influence travel behavior and manage congestion, particularly in developing cities with rapidly increasing motorization rates. A discussion around the similarities and differences of shared mobility between developed and developing countries are provided in Box 3.10 Transformative Technologies in Transportation68 Box 3.9. The Growth of “Super Apps” in Developing Countries In some cases, developing countries are leapfrogging developed countries in the features and level of sophistication of its app-based mobility services. Sometimes referred to as “super apps”, these platforms enable users to access several mobility, payment, retail, communications, and other services from a single digital interface. What distinguishes super apps from MaaS is that super apps can include non-transportation services such as messaging, on-demand video, and telehealth. For example, Gozem is a blended MOD/MaaS smartphone app and transportation service in the Francophone West and Central Africa. What makes Gozem particularly unique is that it integrates a number of mobility, delivery, e-commerce, and payment services (that is, vertical integration). Gozem users can employ the app to (1) dispatch a variety of mobility services (for example, motorcycles/mopeds, auto rickshaws, and taxis); (2) deliver cargo; (3) order groceries, household items, and durable goods; and (4) pay for goods and services using a digital wallet. As of March 2021, Gozem was available in Benin, Burkina Faso, Cameroon, Ivory Coast, Gabon, Mali, Senegal, and Togo. During the first quarter of 2020, the service completed 500,000 rides (Kene-Okafor, 2020). Gojek, which primarily operates in Indonesia, the Philippines, Singapore, Thailand, and Vietnam, integrates shared mobility, parcel and food delivery, moving services, telemedicine, streaming video, mobile payment, and business services into a single platform. The service claims to have 190 million downloads since 2015, more than two million drivers, and 900,000 merchant partners. Grab, which operates in Cambodia, Indonesia, Malaysia, Myanmar, Singapore, Thailand, and Vietnam, also integrates a variety of shared mobility services (for example, e-Hail, pooling, auto rickshaws, bikesharing, and shuttles); food options, parcel, and grocery delivery; and a digital wallet. Other similar super apps include PayTM in India, Careem in the Middle East, and WeChat in China. Image 3.3. Online ticketing service on the go Source: Adobe Stock. Transformative Technologies in Transportation69 Box 3.10. Similarities and Differences of Shared Mobility between Developed and Developing Countries Although shared mobility in the developed and developing countries share a number of similarities, in developing countries these services tend to more frequently evolve from classic factors with a rich history (for example, taxis and rickshaws), whereas in developed countries, more recent forms of shared mobility services started with app-based services, algorithms, or other consumer-facing and/or back-end technologies. Figure B3.10.1 provides a comparative timeline of some key developments in shared and digital mobility over the past two decades. Figure B3.10.1 Comparative Timeline of Key Developments in Shared and Digital Mobility in Developed and Developing Countries (2000–2020) 2000 Contemporary carsharing launches in North America First IT-based bikessharing service launches in North America First e-Hail founded in North America 2010 (2010-13) First e-Hail launches in Southeast Asia, Africa and India IT-based bikesharing launches (2013-14) First MaaS in Southeast Asia demonstration in Europe (2013-14) First super app launches in Southeast Asia (2016) First carsharing, IT-based bikesharing, and use of Drone delivery lauches in Africa (2017) Standing electric scooter sharing founded in North America 2020 Source: World Bank. Transformative Technologies in Transportation70 Box 3.10. Similarities and Differences of Shared (Cont.) A key similarity between shared mobility in developed and developing countries is the desire to integrate trip planning and fare payment onto a single digital platform. However, in some cases, developing countries seem to be leapfrogging into super apps with an array of transportation, retail, lifestyle, and other services aggregated onto a single app-based platform. Services such as Gojek and Gozem highlight this deeper integration. However, at present, these services have not added the types of subscription packages being explored in European versions of MaaS. This could be due to less robust public transit services in some developing cities that can make it difficult for the public sector to play a larger role in the creation of integrated mobility services. Super apps may be able to promote the use of public transportation through the use of gamification and traveler incentives. Gamification is the use of game theory and game mechanics in a super app context to engage smartphone users to employ the app in a particular way. The use of leaderboards, badges, levels, progress bars, and points are examples of gamified applications meant to encourage and/or discourage particular user behaviors. Incentives can also be used to provide a payment or concession to a mobile app user to encourage app use, retention, or some other type of behavior, such as riding public transportation (Shaheen et al., 2016). Although mobility is rapidly evolving in many regions of the world, shared mobility and app- based platforms in developing countries may be evolving differently. First, some countries seem to be attracting the development of many smaller service providers rather than the establishment of a few larger multinational operators. For example, Africa has many relatively small e-Hail and shared ride platforms. This could be due to a variety of reasons such as language barriers, cultural differences, concerns about foreign profiteering, and variations in governance that may make it more difficult for regional and multinational platforms to operate. While private vehicle use and auto ownership tend to be status symbols in both developed and developing countries, shared mobility may also be impacting this dynamic in different ways. In developed nations where per capita incomes are higher, private vehicle ownership can be less of a barrier. Research has shown that shared mobility in developing countries tends to be vastly underused by lower income households. In contrast, shared mobility could offer access to private vehicle use (for example, e-Hail, pooling, and carsharing) in developing countries that would otherwise be economically unattainable due to the high costs of vehicle ownership compared with average per capita incomes. Anecdotal observations from the experts interviewed suggest that shared mobility may be attracting more low- to moderate-income users in a way that it does not in more developed countries. However, shared micromobility may be the exception. Often, safe infrastructure for pedestrians, cyclists, and scooter users is limited in developing countries. This could hinder the growth and mainstreaming of shared micromobility options. Finally, shared mobility is changing traditional labor roles, creating new employment opportunities, and disrupting incumbent industries. Shared mobility has the potential to affect labor on job opportunities, as well as upward and downward wage pressures. For example, shared mobility is contributing to employment growth in some sectors of transportation, such as by encouraging demand for e-Hail drivers, but it may also be disrupting existing employment where demand for other services may be declining (for example, taxis). However, this dynamic is not new to developing economies where in some countries, informal transportation services attempt to undercut more formal modes through lower wages and user costs. Transformative Technologies in Transportation71 Electrification Electric mobility has garnered growing interest and significant momentum across several major global markets—often motivated by transportation sector decarbonization. Europe, China, and the U.S. together account for more than 90 percent of the world’s EV fleet (IEA, 2022), and with increasingly relevance for LMICs. Growing interests in EV are often motivated by decarbonizing the transportation sector, but the rationale for the transition is much wider for LMICs. It has the potential to reduce local air pollution, improve the quality of public transportation, provide last-mile connectivity, reduce dependency on imported fuels, and provide opportunities to participate in vehicle supply chains. However, e-mobility alone will not resolve all the mobility challenges. It must be part of a comprehensive program to promote sustainable and inclusive urban mobility. The climate and environmental impact of transportation cannot be ignored; the sector currently accounts for approximately 25 percent of all GHG emissions and contributes significantly to cities’ air pollution crisis. Although, mobility is fundamental to development, mobility and emissions do not need to go hand in hand. In this context, the adoption of EVs is a critical instrument to decouple mobility and growth, from emissions. However, the upfront cost of EVs remains higher than the traditional alternative, and it acts as a barrier to adoption, particularly in lower-income populations and countries. As EV technologies evolve and costs reduce, wider adoption might be economically viable when EVs’ lifetime advantages (such as reduced maintenance and operation costs, and environmental benefits) are considered, market segments near cost-parity are given priority, and innovative financing structures are made available to overcome cost barriers (see Box 3.11). EVs will eventually dominate the passenger transportation systems of all countries, but the timing of this transition will be determined by the economic and financial realities of each case. The complex choices and policy questions have rarely been considered from an LMIC perspective. A recent report by the World Bank conducted a detailed analysis of EV adoption across 20 LMICs (Briceno-Garmendia, Qiao, and Foster, 2023), reflecting various country experiences. Overall, the study reveals significant opportunities to scale up passenger transportation electrification in developing countries when it is embraced as part of an integrated sustainable transportation agenda. The study results demonstrated strong economic cases for EV transitions in many LMICs. In particular, electric buses and two- and three-wheeled vehicles are effective entry points. Also, EV uptake reduces GHG emissions in all the countries studied, even in those where electricity is produced by fossil fuels. This is largely because EVs offer a major energy advantage due to the efficiency of their engines. On average, petrol vehicles consume four to five times as much energy per vehicle-kilometer as EVs. The energy advantage tends to be larger for LMICs with old and relatively inefficient ICE fleets, and the adoption of EVs can bring GHG reduction even before the power grid is fully decarbonized. EV transition is not a panacea to obtain sustainable mobility. Governments should promote policies that reduce the need for travel, make nonmotorized transportation safer, and make public transit use cheaper and more convenient. EV policies should therefore be embedded in broader sustainable transportation strategies such as the avoid-shift-improve paradigm. It is worth noting that electrification is a key component, but not the only option to improve energy efficiency and decarbonize the transportation sector. Although the EVs based on lithium battery are popular in the market, the development and deployment of FCEVs based on hydrogen is also making progress. As the manufacturing and operation costs drop further, the uptake of FCEVs could also accelerate, particularly in the long-haul freight market. This is true for both the road traffic sector and other transportation modes. For example, the sustainable aviation fuel, a renewable or waste- derived aviation fuel that meets a set of Carbon Offsetting and Reduction Scheme for International Transformative Technologies in Transportation72 Aviation (CORSIA) sustainability criteria, is a more practical carbon reduction pathway and can reduce up to 58 percent of aviation GHG emissions compared with business as usual in 2050 (Malina et al., 2022). Liquefied natural gas (LNG) is frequently discussed as a fuel pathway toward greener maritime transportation (Englert et al., 2021a), although it may not be viable in the long term. Biofuels, hydrogen and ammonia, and synthetic carbon-based fuels are also promising alternatives to achieve the zero-carbon objective in maritime transport. Therefore, electrification should be considered in the broader context of zero-emission energy alternative and may be complemented by other energy sources in different niche market. Box 3.11. Electrification of Public Transport: A Case Study of the Shenzhen Bus Group EVs offer a viable option of reducing emissions from the transportation sector with the support of low-carbon power sources. Compared with the complex landscape of promoting EVs in the consumer market, the public sector is usually the owner and operator of bus companies, and it should be easier for them to promote e-mobility in the public transit system. However, converting to e-mobility in the public transit system is still slow, particularly in developing countries. Shenzhen, a coastal city in China, began adopting electric buses in 2009. Eight years later, Shenzhen became the first city in the world that fully electrified its urban transit fleet of 16,359 buses. Although some local conditions are unique, the experience in Shenzhen may still offer lessons for other cities that aspire to promote e-mobility. The Build Your Dream Company Limited (BYD), a giant vehicle manufacturer with an early move to electrification, is headquartered in Shenzhen. The city has attracted many high-tech companies and research institutions, forming an almost complete supply chain, from battery production and vehicle manufacturing to battery recycling and research and development. Therefore, the public sector has built strong institutional capacity to support the transition, in close partnership with the private sector. The Shenzhen Bus Group Company (SZBG), a major bus operator in the city, conducted a thorough market study that covers a wide range of factors, including vehicle performance, operation and fleet management, maintenance and repair, and human resources. It selected DC fast charging technologies for bus operation instead of the slower AC charging because of two prominent issues: charging speed and the lack of space at depots. The SZBG also considered several alternative charging technologies such as battery swapping and wireless charging but did not choose those due to various reasons including technical constraints, financial viability, charging efficiency, and impact on the grid. To reduce initial investment and improve financial viability, the SZBG adopted a leasing model in which it rents buses and charging stations instead of directly owning them. This model helps to lower costs through market competition and encourages the participation of the private sector. A posterior analysis shows the total cost of ownership (TCO) of electric buses is 35 percent lower than the diesel fleet with subsidies, while the TCO is 21 percent higher if subsidies are excluded. To maximize the benefits, the SZBG also upgraded its Intelligent Transportation Center, which operates a bus operation management system, a safety management system, and a repair and charging management system. This integrated system allows the real-time monitoring of bus operations and charging status, which helps reduces drivers’ range anxiety and also improves operation efficiency and safety. A comprehensive and well-planned training for all staff in the SZBG made the electrification transition a smooth process and not a single employee was laid off. The environmental benefits are substantial. A posterior analysis shows that the lifecycle GHG emission of an electric bus is only about 52 percent of the emission from similar-sized diesel buses in Shenzhen. The electrification of the SZBG buses saves 194,000 tons of CO2 annually, and at the same time, significantly reduces other pollutants such as CO, NOx, PM10, and PM2.5. Transformative Technologies in Transportation73 Box 3.11. Electrification of Public Transport: (Cont.) The experience in Shenzhen shows that the electrification of public transit can yield huge environment benefits. It can be financially sustainable with a mild government subsidy, which could be justified by the additional social benefits associated with it. As the economies of scale grow further (the private charging stations in Shenzhen start to offer services to electric taxis and private vehicles in 2018), the need for government subsidy can be further reduced. It was also demonstrated that the institutional capacity from the public sector is critical for the success of the transition. The management must understand the pros and cons of the technologies and work effectively with the private sector to address the barriers that emerged in the process. From that perspective, a timely documentation of these early experiences, particularly in the context of developing countries, could be very valuable. Policy Implications The adoption of the CASE technology is expected to have a significant influence on the transportation sector. Embracing technological changes requires a mindset of openness and adaptability, particularly in harnessing the potential of emerging technologies such as AI for sustainable and inclusive development. Social equity should be a key consideration, ensuring that transportation systems prioritize affordability, accessibility, and equality. Additionally, the impact of technology on the labor market requires proactive preparation, including workforce development programs to support individuals affected by automation. Leverage the Opportunities Brought by CASE to Advance Future Mobility All indications point toward more, not less, travel, as connectivity creates more, not less, opportunities for personal engagement and economic growth. It is no longer a question of whether CASE technologies will occur, but rather when, at what pace, and in what form. The implications of CASE technology extend to multiple interdependent levels: the supply of mobility services; demand and behavioral changes; system operational performance, and use of alternative energy sources. On the supply side, the technology is expected to introduce entirely new modes of mobility in the form of SAV fleets, in addition to improving multiple aspects of current mobility options. Such improvements include high automation of certain, or all, driving tasks from origin to destination, and supporting travel-related decisions by providing real-time information through wireless telecommunications. On the demand side, the availability of new mobility forms in addition to the improvements to current transportation systems through connectivity can affect the activity patterns and mobility choices of travelers. Those changes can involve household-level decisions, such as owning a car, or individual decisions such as departure time and route choice. Changes to both supply and demand in addition to the improvements brought by the technology to traffic flow ultimately affect the operational performance of transportation systems. Furthermore, electrification enables the use of alternative energy sources to power vehicles, including renewable energy sources such as solar and wind. This diversification reduces dependence on fossil fuels and provides opportunities for a cleaner and more sustainable energy mix. By leveraging these CASE technologies, developing countries have a unique opportunity to decouple economic development from increasing congestion, pollution, and GHG emissions. The use of shared and on-demand mobility in developing economies has the potential to expand access to jobs and Transformative Technologies in Transportation74 critical services, while providing new and growing employment opportunities in the transportation sector. Governments in developing countries need to proactively guide sustainable and equitable deployments of mobility policies. This entails aligning technology adoption with equitable outcomes, fostering collaboration between public and private sectors, seeking inputs from local experts and stakeholders, and tailoring policies to local contexts to create transportation systems that promote accessibility, sustainability, and equitable economic growth for all. Enhancing Mobility Services Provision Through CASE Deployments Lessons from deployments in developed countries have shown that the impacts of CASE technologies vary considerably based on various local factors, such as the built environment, existing public transportation service, and congestion levels. It is clear that the impacts of CASE deployments in developing countries will undoubtedly vary, requiring tailored policies that account for local social, economic, urban planning, and governance contexts. A key question from an institutional standpoint, especially in emerging economies where some of these mobility innovations are just beginning to emerge, relates to the governance structures and regulatory frameworks for emerging mobility services. This warrants immediate awareness and engagement by respective agencies: • From the standpoint of infrastructure provision: ₀ Major improvement is required in management and operation systems to allow them to accommodate requirements of mixed traffic and heterogeneous users. ₀ Infrastructure deployments must integrate road transportation assets with infrastructures of connectivity, sensor, data, EV charging, intelligent traffic management, cybersecurity, and so on. ₀ Development of new software platforms is essential for smart and connected communities, as this currently presents a major deployment bottleneck. ₀ The advent of new technologies creates opportunities to facilitate new and potentially more effective models of ownership and operation, reflecting more flexible forms of service delivery through greater involvement of the private sector and public-private agreements. • From the standpoint of mobility service provision: ₀ Transit agencies should undertake a dramatic restructuring and rationalization of transit networks, focusing on high frequency, high capacity, high service quality services (rail, BRT), and should rely on SAV fleet services for local area travel and access to high-capacity lines. ₀ Transit agencies can act as MaaS providers, shifting the focus from exclusive transit to a broader perspective of mobility. This can involve owning AV fleets, including small buses, and providing some form of SAV operation, or contracting with third parties to provide these services with varying degrees of control. Alternatively, agencies can facilitate the development of preferred and profitable business models by the private sector while ensuring intermodal access. ₀ Private sector engagement and collaboration with application developers should be facilitated by making data readily available in standard formats. ₀ Co-branding strategies can be considered to leverage location and traffic advantages, rethinking the “product” from a user experience perspective. Transformative Technologies in Transportation75 Promoting Social Equity and Addressing Disparities in Access to Mobility Social equity is an active commitment to fairness, equality, and access related to planning, policy, and implementation of mobility services. Common user equity challenges are associated with the disparities in access and usage patterns among different groups. Typically, users who benefit the most from these technologies tend to be younger and more educated, and have higher incomes. On the other hand, older adults, low-income individuals, rural households, and minority communities may face barriers in accessing the necessary tools and services, such as the internet, smartphones, and banking services. Public policy, including legislation and regulation, can play a vital role in mitigating these disparities and ensuring equitable access. In some cases, innovative and emerging transportation technologies can help overcome equity challenges while in other cases, it could exacerbate existing challenges and create new ones. The public sector will need to actively pursue corresponding policies to ensure that these transportation services do not reinforce existing disparities. Identifying, understanding, and responding to the various user and labor equity challenges will be key in helping ensure accessibility, affordability, and job access for all. A mobility policy evaluation framework (see Annex 2) for transportation access equity would help the public sector to identify, understand, and overcome spatial, temporal, economic, physical, and social barriers. Public agencies may develop targeted policies intended to provide shared mobility options and other alternatives to private vehicle ownership. Policies could also target improvements in the physical infrastructure across an array of built environments intended to address infrastructure gaps (for example, lack of curbs and bike lanes) and new charging infrastructure to encourage the transition to EVs. Public agencies could also adopt policies that address key economic challenges, such as subsidies and other incentive programs for zero-emission vehicles, and micromobility. Policies could also focus on improving the quality and affordability of public transportation options, such as greater geographic and temporal coverage and integrated fare payment that allows travelers to bundle first- and last-mile services with public transportation tickets. This represents a few ways the mobility policy evaluation framework can be applied to challenges commonly associated with mobility in developing nations. In addition, P3 can also play a significant role in addressing equity challenges and specific use cases. Collaboration in areas such as first- and last-mile connections, low-density service, off-peak service, and service for people with disabilities can bridge spatial and temporal gaps, enhancing access to public transportation and providing cost-effective services for underserved populations. By applying this framework, stakeholders in developing countries, including the public and private sectors, can work together to identify and eliminate barriers, ensuring that innovative and emerging transportation technologies are accessible to all segments of society. Proactive Policy Planning to Address Labor Market Impacts From CASE It is crucial for policy makers to proactively address the potential impacts of shared mobility, CAV, and fleet electrification on the labor market. This includes closely monitoring wage pressures, examining the specific effects on different types of drivers and maintenance staff, and implementing workforce development programs to prepare individuals for changing job dynamics. Policy debates often revolve around whether these advancements create new employment opportunities or simply shift jobs from one service to another. Concerns about labor exploitation and underpayment of “gig” workers have also emerged. Downward wage pressure can occur when Transformative Technologies in Transportation76 users are attracted to services or platforms that pay lower wages than incumbent services. In developed countries, this has commonly occurred when users migrated from taxis to TNCs. The more open marketplace and lower barriers for entry for drivers can also contribute to increases in the supply of drivers. The electrification of the fleet is expected to result in the demand for a new set of maintenance skillsets compared with the traditional vehicle maintenance jobs. These impacts from shared mobility, electrification, and vehicle automation on labor are still uncertain and debated, particularly in developing economies. In addition, governments will need to understand how vehicle automation may affect drivers (for example, taxis, public transit employees, and informal transport). Drivers may face changing job responsibilities and trip demands due to vehicle automation. A study of the impacts of vehicle automation in developed economies found that the benefits of Level 4/5 AV deployment outweighed the adverse impacts on the existing workforce (Eno Center for Transportation, 2018). The study found that, in a single year, the benefits gained from widespread AV deployment could be greater than the total costs to workers over the next 35 years combined. Potential economic benefits include improved safety, increased commuter productivity, reduced energy consumption, and enhanced accessibility. To prepare for the potential impacts of AVs on the labor market, governments should consider implementing workforce development programs that offer job training for drivers who may be adversely affected by vehicle automation. Such programs can equip former drivers with the skills and knowledge needed for alternative employment opportunities. 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Process Focus on improving Node transportation methods Guideway Focus on facilities, terminals, through different business and centers within models, enhanced back-office Improve infrastructure along Conveyance which products move, transportation networks, tasks, and changes in impacting overall operations government policies Improve vehicles used to enhancing safety, efficiency, move goods, leading to and usability for vehicles and increased capacity, lower freight transportation operating costs, faster speeds, and better service Adopting Innovation Framework for assessing technology adoption in freight transport Direct Benefits Areas of Innovation Focus on improving utilization, productivity, and Evaluate innovations in conveyance, guideway, effectiveness of freight transportation systems node, or process Relevance to Region Supporting Systems Assess if the innovation addresses specific Consider the presence of financial, legal, political, problems or opportunities existing in the region infrastructural, and educational support to facilitate innovation adoption Advanced Train Digitization Electric Trucks Drones Blockchain Control System Assessment of technology innovation in freight transportation Automation in Autonomous Internet of Next-Generation Zero-Carbon Freight Handling Trucking Things (IoT) Air Control Bunker Fuels Policy Implications Apply systematic assessment to evaluate technology adoption potential in developing countries Embrace the local context for technology adoption Foster innovation through experimentation and partnerships Align incentives across public and private sectors Transformative Technologies in Transportation83 Chapter at a Glance For an innovation in freight transportation to be transformative, it needs to be widely adopted. To be widely adopted, it needs to improve, or offer the potential to improve, the utilization, productivity, and effectiveness of the conveyances, the guideways, nodes, or the processes. For a successful technology deployment in a specific region, the innovation needs to be relevant to the region in addressing problems or realize opportunities existed in the region, and enable supporting systems including financial, legal, political, educational, and civil infrastructure development. This chapter examines how innovations in freight transportation can be analyzed in terms of their potential to transform the sector. It presents the different types of freight innovation with examples. The chapter includes a conceptual framework for assessing innovation adoption and impact to identify both direct and indirect benefits arising from the innovations. It can assist in policy recommendations to improve the overall performance of the freight transportation system. Transformative Technologies in Transportation84 Context Transportation is a relatively unique economic activity. By trading space or location with time, transportation has the potential to dramatically increase the value of an item. It is susceptible to surges in demand as there is significant opportunity cost to reserve transportation capacity for use in times of peak demand. Historically, the transportation of freight, passengers, and even information had the same characteristics, traveled on the same conveyances, and moved at the same speed. This has changed over time as these three flows have very different characteristics and are managed almost totally independently of each other. Information, for example, once moving only as fast as a horse or a sailing ship, is now essentially instantaneous and completely decoupled from physical transportation. Innovation in freight transportation is closely tied to the overall economic development of most countries. The constant improvements in how a product can be moved within a country or across the globe allow for increased trade between countries and tend to improve the quality of life. Some transportation innovations are evolutionary, in that they improve things incrementally, whereas others can be revolutionary by transforming regions, economies, or cultures. Direct versus Indirect Impacts of Innovation. The results from an innovation can be either direct or indirect. Direct benefits are those things that the innovation immediately impacts. These are usually easy to measure and estimate, and the better the immediate and direct impacts are, the faster, is the adoption and assimilation of that innovation. For freight transportation, the direct benefits of innovations are typically a reduction in costs, a lowering of transit time, or an increase in carrying capacity. These improvements, in turn, generally lead to higher shipping volumes along that lane, route, or corridor. Indirect benefits range widely and tend to be more transformative. They can include changes in trading patterns, increased economic development, integration of distant locations and societies, and so on. Indirect benefits are more difficult to identify at the early stage of an innovation. They are, in many cases, unintended and unforeseen and can take years to emerge. Amara’s Law states that people tend to overestimate the impact of a new technology in the short run, but to underestimate it in the long run (Ratcliffe, 2016). Usually a formal cost-benefit analysis of each new technology would be more accurate than a simplistic distinction of direct (or immediate) and indirect (longer-term) impacts. However, there are deeper and more consequential longer-term impacts than those that are achieved during the initial use of a technology, which are not necessarily additive. For any technology to be truly transformational, it must be widely adopted. And to be widely adopted, it needs to have sufficient direct benefits to spur adoption. Dimensions of Process Improvements. Freight transportation is essentially a process, taking in specific inputs (fuel, labor, and equipment) to produce a set of outputs (a new location at a certain time in the future). The improvement of processes can be categorized as follows: utilization, productivity, and effectiveness (Caplice and Sheffi, 1994). Any innovation that provides direct benefits has to increase utilization, productivity, and/or effectiveness. This applies to both incremental improvements and large transformational leaps. Utilization is the ratio of the amount of a resource consumed over what is available. Think about a ship or a truck that is loaded to half capacity. For example, the introduction of lightweight smart- sized packaging that allows for more individual items to be loaded within a container will lead to the higher utilization of that resource. Higher utilization leads to a lower transportation cost per item. Transformative Technologies in Transportation85 The same holds true for the utilization of trucks and transload or sorting facilities. Better utilization is the reason why container ships continue to increase in size, as the “on-the-water” unit cost decreases with additional capacity even though this complicates loading and unloading. Productivity, or efficiency, is the ratio of the quantity of output produced over the quantity of input used. Fuel efficiency in a truck, for example, is miles per gallon (kilometers per liter). Sometimes, the metric is inverted to quantity of inputs over quantity of outputs, such as cost per distance. The cost involved is simply the sum of the labor, equipment, and other inputs costs required for the process to create certain outputs. One can also think of productivity when trying to reduce negative externalities, such as GHG emissions. Effectiveness is different from the others in that it is focused on the quality rather than the quantity of the output. It can be thought of as a comparison to a standard, such as the damage rate or days of transit. A shipment mode that takes two days is generally seen as more effective than one taking three or more days, with all other costs and things being equal. Similarly, reducing the variability of shipping from three days to one day is an improvement in the quality or effectiveness of the transportation move as it can lead to lower inventory levels along the supply chain. Image 4.1. Electric truck being charged from the charging station Source: Adobe Stock. Transformative Technologies in Transportation86 Types of Freight Innovation In general, innovations in freight transportation can be classified into one or more of the following four categories: conveyances, guideways, nodes, and processes. Each is discussed in this section. Conveyance Innovations Innovation at the conveyance level occurs when the vehicle that used to physically move the goods is improved or enhanced. The improvement could be increased capacity (large container ships), lower operating costs, faster speeds, better service, or a combination of these things. A classic example is steam ships. First introduced in 1809, the technology was initially dangerous and quite expensive. Over the next decade, however, improvements continued to be made and in the 1820s, steam ships were fully used in major waterways. This had significant impact on river transportation specifically. The cost of using the other alternative mode, horse- or oxen- pulled wagons, became exceptionally high in comparison – wagon rates were three times higher for upstream steamship rates and 2,500 percent more than downstream rates. Between 1815 and 1860, the cost of moving 100 pounds of product from New Orleans to Louisville dropped from $5 to $0.25. As the technology improved, steamships eventually replaced the clipper ships on transatlantic moves. A steamship could carry more, was cheaper, and faster than wagons. Essentially, it improved the utilization, productivity, and effectiveness. The indirect benefits of the steam ship include expanded global trade and deeper development into hinterlands. Another example of conveyance innovation is the containerization movement. The standardization of 20- and 40-foot containers enabled goods to move seamlessly among trucks, trains, and ships without the need for material handling at terminals. Shipping processes in all countries quickly evolved to enable intermodals. Utilization of transportation assets rose as containers could be stacked and loading and offloading times were shortened. Labor productivity at terminals rose with less material handling. Shorter and more reliable shipping times effectively reduced inventories for shippers. The container was also more effective in offering benefits such as increased security from origin to destination and the option of temporary storage upon arrival. The indirect benefits of the container include further expansion of global trade and deeper development into the hinterlands away from navigable waterways. A more recent example is EVs that continue to create conveyance innovations for freight transportation. Rapid advances in battery technology mean that evolution to EVs has already begun for smaller trucks, with larger tractors waiting on the sidelines. They offer several benefits such as lower energy cost (better productivity), reduced maintenance (better utilization and productivity), and environmental improvements (better effectiveness). Guideway Innovation Innovations to the guideways are essentially infrastructure improvements over the routes that the product moves. The German Autobahn network and United States Interstate Highway System highlight the innovation of guideway systems. Nationally unified standards for construction, controlled access, and signage improved safety, efficiency, and usability for vehicles of all sizes and provided significant gains for 18-wheelers moving freight. The system design also spurred innovation in civil engineering and construction technologies to effectively meet the standards. The complementary nature of containerization and the interstate highways enabled trucks to easily continue the intermodal chain far inland, which highlights how innovation can accelerate through positive feedback among multiple technologies. Transformative Technologies in Transportation87 A potential new guideway innovation is the hyperloop, which can move both freight and passengers in pods that move through a network of point-to-point sealed tubes. The low air pressure within the tubes allows the pod to travel at speeds of more than 600 miles per hour (Beyer, 2021). There are several ongoing pilots and experiments for testing the engineering and economic effectiveness of the concept. The primary challenges include securing land rights, construction costs, and cost-effective operational models. It is doubtful that this technology is suitable to developing countries, as it is exceptionally cost prohibitive. Node Innovations While transportation is generally concerned with movement along the arcs of a network, operations at the facilities, terminals, and centers that connect these arcs can have significant impact on overall operations. Within a supply chain network, facilities are used either to provide storage for inventory or to improve flow by enabling better consolidation. A classic example of node innovation is the concept of cross docking, where the product from inbound conveyances of one mode are sorted and placed onto outbound conveyances of the same or a different mode, with little or no storage in between. Cross docking has been widely used in the less-than- truckload industry since the 1930s but only gained widespread use in the 1980s following its adoption by Walmart. It is now common practice for most retailers to ensure their fast-moving products spend minimal time in storage. It requires sophisticated tracking systems (either digital or analog) as well as a finely coordinated schedule. It is not a stretch to consider that most passenger transportation that involves terminals (airports, rail terminals) utilizes cross-docking, in that the products (people) self-sort and spend minimal time at the facility, unless there is a major weather event. A more recent modification to this concept is transloading nearby ports. The idea is that the product shipped in international forty-foot equivalent units (FEUs) or twenty-foot equivalent units (TEUs) containers are received at a terminal and transferred into domestic containers (53’ in the U.S., for example) for outbound moves. This does two things. First, it keeps the FEU/TEU closer to the port where it can be sent back for movement or reloading. Second, it leads to fewer inland transportation moves as three FEUs can be repacked into two 53’ domestic containers. Process Innovations These are innovations that improve the methods by which goods are transported, such as a different business operating model, an improvement in back-office tasks, or changes in government policy. Relay-as-a-Service (RaaS) is one such innovation that has evolved over the centuries. Introduced in 1860, the U.S. Pony Express was an operating model for transporting mail and other parcels long distance by handing it off or relaying it between multiple vehicles (riders and horses). The system consisted of 186 terminals placed at roughly 10-mile intervals, stretching about 1,900 miles. The entire distance could be covered within 10 days using multiple riders handing off the mail. Interestingly, the concept of relays is still considered an innovation today. Rivigo, in India, now uses essentially an identical operating model as the Pony Express. Essentially, the trailers keep moving towards their destination while the drivers work in round trips. Another manifestation of this same concept is called the Physical Internet (Montreuil, 2011), where all goods are packed within a set of fully modularized and standardized cases and containers that are moved across networks, similar to how information packets are transferred from server to server in the digital internet. Essentially, the “physical” packets are relayed between terminals, so that the goods continuously move while the power units hand off the packets as with the Pony Express (see Box 4.1). Transformative Technologies in Transportation88 Box 4.1. Rivigo’s RaaS System in India Founded in 2014, Rivigo is a transportation company operating in India that uses driver relays to not only increase the velocity of the goods being transported, but also to reduce the time away from home for their drivers. The Rivigo system consists of over 70 relay stations across India. The stations are about 200 to 300 km apart, which translates to about five hours of driving time. The idea is that each pilot drives a truck from their home pit stop to the next, where a new pilot takes over and the original driver drives a load going back to their original location. This is sometimes also called “slip seating,” where multiple drivers share the same truck. A variant is “drop & swap,” where instead of trading trucks, the two drivers exchange their trailers or containers. The primary benefit is that the drivers stay relatively close to their home station instead of being on the road for weeks or months at a time, while the truck itself does not stop. In fact, relay-led operations can reduce transit times by 50 to 70 percent as compared to conventional transportation means. For example, a shipment from Delhi to Bangalore, which are about 2,200 km apart, normally takes five days by a traditional truck. Using the relay system, this shipment can be delivered within 48 hours. The truck traveling from Delhi to Bangalore and back would use 16 different drivers over the roundtrip of about 100 hours. India is facing a driver shortage currently with an estimate of just 450 drivers for every 1000 trucks by 2022. Driving is not a desirable profession, as it requires long hours away from home. Rivigo hopes to make the driving profession more desirable by making it more predictable. Rivigo’s RaaS system relies on the use of patented technology that communicates and selects the right driver for each move. The selection is based on several parameters such as equity of driving time, hours of rest, total drive time, driver performance, fuel use, and so on. Digital load matching. A more recent example of a process innovation is the growth of digital load matching. In addition to identifying a candidate carrier, the shipper needs to officially tender the load to the carrier and agree upon price as well as any modifications. The carrier needs to recommend (or accept) a price, assign an asset to the shipment, and schedule operations. Many brokerages and third parties might communicate digitally (via the web, email, or even an API) but all of the surrounding tasks are performed manually using phones and text messaging. Even still, digitizing or automating at least some of the shipment process is a welcome process improvement. Motor Carrier Act (MCA) and Deregulation. The direct impact of deregulation on freight transportation was to reintroduce competitive forces to an industry that had been protected for decades. Another example of process innovation is the MCA of 1980 that deregulated the inter-state motor carrier industry within the U.S. (Caplice, 1996). Prior to 1980, all surface freight transportation in the U.S. was highly regulated. Trucking firms were required to obtain authorization for hauling by both commodity and route. The process to obtain these authorizations was both costly and time consuming. Additionally, shippers with private fleets were not allowed to haul other shippers’ freight and for-hire carriers were restricted to being either contract or common carriers. Common carriers could offer transportation to the general public but were required to charge the same tariffs to customers with similar freight, that is, they could not discriminate between shippers. Contract carriers, on the other hand, could serve specific customers but were not allowed to carry general freight from other shippers. Besides, the number of customers a contract carrier was Transformative Technologies in Transportation89 allowed to serve was limited to eight. The net effect of these rules was the protection of the existing motor carriers through extensive barriers to entry. Upon enactment of the MCA, there was an almost immediate entry of small, entrepreneurial, and primarily non-union carriers into the marketplace. The number of carriers registered with the Interstate Commerce Commission (ICC) rose from 16,874 in 1980 to 54,480 in 1994 and to over 300,000 in 2021 (Department of Transportation, 2020). Average freight transportation costs dropped by almost 50 percent within eight years. The indirect impacts from deregulation include the bifurcation of the freight market into full truckload and less than truckload segments – with very little overlap. The reduction in trucking transportation rates spurred increased product flow across the country. Deregulation is a great example of where there are both winners and losers when an innovation is introduced. Making a system more efficient may improve the situation for the end users of the system, but can hurt others within the system. While an apparent boon to the shippers, these rate wars lead to decreased profit margins and bankruptcies for many carriers – especially smaller firms. There are also many cases where innovation or enhancements to a process creates overall winners. In freight transportation, improving scheduling at ports or large distribution centers leads to reduced waiting time and dwell, increases predictability, which in turn boosts a driver’s efficiency and reduces congestion. The need for such process innovation is also continuous, as evidenced by global supply chain bottlenecks in 2021. At a time when the queue of container ships waiting offshore in Southern California reached a record 73 ships, the Port of Los Angeles reported that 30 percent of trucking appointment slots for transferring cargo went unused each day, on average (Berger, 2021). There are no explicit “losers” here. Instead, the resistance to doing this is often simply inertia and the party that needs to make the change is not feeling the pain or paying the cost of the inefficiency. This is the case for dwell; a warehousing function is usually in charge of the scheduling, and they do not feel the pain of congested yards. To summarize the above section, for an innovation in freight transportation to be transformative, it must first be widely adopted. To be widely adopted, it needs to improve, or offer the potential to improve the utilization, productivity, and/or effectiveness of the conveyances, the guideways, nodes, and/or the processes. In the next section, a framework is introduced to assist in assessing the potential adoption and impact of an innovation. Image 4.2. Using Online systems To Check Inventory Source: Adobe Stock. Transformative Technologies in Transportation90 Framework for Assessing Technology Adoption in Freight Transportation There are two primary dimensions of the framework: the types of direct benefit (utilization, productivity, and effectiveness) and the areas that benefit (conveyance, guideway, node, or process). Two additional dimensions are added to these to complete the framework to assess innovation adoption: the characteristics of the region and the supporting systems. Innovation does not occur uniformly across the globe, neither does the path of innovation follow the same course in all regions. Consider the path that personal telecommunications took in Latin America and Africa compared to North America. Those regions are far more advanced than the U.S. in terms of mobile payment and communication. The U.S. had to deal with the long tail of earlier investment into now outdated technology (copper wires). Sometimes, not rapidly adopting a certain technology allows for a country or region to leapfrog to a newer, better technology without having to undo entrenched practices. Innovation needs to solve or improve a problem that exists in a region where transportation can have a significant impact on economic development, and they have to be contextualized for local relevance. Regions have different challenges and opportunities. For example, increased automation in material handling is important in North America and parts of Europe since it reduces the amount of manual labor required – an expensive input in these regions. This is not the most pressing problem in parts of South Asia and Africa, where labor is neither expensive nor scarce. To be relevant, the innovation needs to address problems or realize opportunities that exist in the region. Innovation also does not happen in a vacuum. It generally requires supporting systems – legal, financial, political, and infrastructural that allow it to be adopted. Without some level of support, the innovation will not have an opportunity to flourish. For example, an innovation requiring the use of smartphones for improved loading and scheduling requires widespread connectivity and the ability of drivers to have smartphones. Such supporting systems include financial, legal, political, educational, and civil infrastructure development. Essentially, the framework boils down to four questions. The first two deal with the general benefits of an innovation while the last two are concerned with local or regional adoptability. Benefit: How does the innovation improve the utilization, productivity, and/or effectiveness of a 1.  freight transportation system? Application: Where does the innovation improve a transportation system in terms of its 2.  conveyances, guideways, and/or processes? Relevance: Is the problem being addressed by the innovation relevant to the region being 3.  considered? Systems: Is there sufficient support to enable innovation adoption in the region? Conversely, are 4.  there political, social, or other systems that are obstacles to adoption? For this framework, we first assess the innovation potential of various technologies and then assess the adoption potential in developing countries. The first two questions provide the framework for describing and characterizing potential innovations. The third and fourth questions provide the framework for assessing the adoption of these potential innovations in a developing country context. Transformative Technologies in Transportation91 Box 4.2. Economic Impact of Freight Transportation in the Context of Land-locked and Developing Countries A natural place to consider for freight transportation to have a significant impact on economic development are landlocked countries that are dependent on coastal or transit countries to engage in maritime trade and connect with global markets. They are extremely vulnerable to border closures and restrictions. Studies have shown that longer distances from ports result in longer overland transportation and reduces growth (Ndulu, 2007). Of the 44 landlocked countries globally, 32 are developing countries. For these landlocked developing countries (LLDCs), the import and export times are nearly twice of those of transit countries and the average costs of exporting a container are 60 percent higher than in transit countries (Dumitrescu, El-Hifnawi, and Zemke, 2018). In 2014, the World Bank estimated that LLDCs spent $3,204 to export a container of cargo, compared with $1,268 for transit countries and $3,884 for importing a container, compared with $1,434 for transit countries.16 As of 2018, LLDCs’ share in global freight transported by road, air, and rail were 1.15 percent, 1.05 percent, and 2.09 percent , respectively. LLDCs’ share in global exports was only 1.01 percent in 2019. Of the 32 LLDCs, 16 are in Sub Saharan Africa (SSA) and 12 are in Asia, with most of these having trade corridors through South Asia.17 Thus, to assess the role of transportation technologies in developing countries, the transportation barriers to trade and the general population suggest a focus on the regions of SSA and South & Central Asia (SCA). While the support systems for innovation will differ between the two regions, this study provides an overview of the key characteristics of the financial, legal, political, educational, and civil context in these regions. Financial systems in these regions are best characterized by the size of the informal economy. The majority of households and businesses operate informally and lack the bank accounts and financial records to access loans with affordable rates. While microfinancing has grown, access to finance remains a significant barrier to investment in innovation. Moreover, scarce transactional data and low financial literacy limits the potential for process innovation. Legal systems provide the foundation for commercial and civil engagement. The 2021 World Justice Project Rule of Law index,18 which considers a broad range of factors, ranks all countries in South Asia and 25 of 33 countries in SSA below 70. Poor rule of law inhibits innovation by limiting risk-sharing and risk-pooling contract mechanisms. It also raises risks for global transportation providers to establish operations and investment. Political systems enable all other support systems and are difficult to assess. Legislative processes in these regions are often slow. A consistent aspect is low resources and incentives to enforce effective regulations, which can have a direct impact on transportation. For example, lacking effective enforcement of weight limits on roads is leading to poor quality of roads and slow transit times. Inconsistency in standards and processes can cause delays at borders, and low enforcement can result in unofficial checkpoints that exacerbate delays. 16 https://www.un.org/ohrlls/content/about-landlocked-developing-countries 17 https://unctad.org/topic/landlocked-developing-countries/map-of-LLDCs 18 See https://worldjusticeproject.org/sites/default/files/documents/WJP-INDEX-21.pdf. Transformative Technologies in Transportation92 Box 4.2. Economic Impact of Freight Transportation (Cont.) Educational system support for the transportation and supply chain profession is relatively low in these regions. Qualified logistics-related labor is in short supply from truck drivers to senior supply chain management positions. Comparatively high skills deficits of between 20 percent and 30 percent at all job levels were reported in South Asia and SSA. Professional education is lacking, as companies in developing countries rely twice as heavily on internal training (17 percent) compared to their counterparts in the developed world (9 percent) (McKinnon et al., 2017). Also, there are limited supply chain degree programs. Civil infrastructure development provides the operational environment for technology adoption. In the 2018 Logistics Performance Index, SSA and South Asia scored low on road and rail quality; SSA scored relatively well on ports and airports while South Asia had low airport quality (Arvis et al., 2018). Expensive and unreliable electrical power constrains the supply chain, from fundamentals like manufacturing to adoption of new technologies like EVs. Communications is a bright spot, as mobile cellular subscriptions in LLDCs have grown to an average of 78 subscriptions per 100 people in 2018.19 Mobile technology provides opportunities for step changes to new paradigms such as digitization of freight and the “sharing economy” to increase productivity and workforce engagement. Assessment of Technology Innovation in Freight Transportation In this section, a variety of selected technologies are examined to assess their potential to drive innovation in freight transportation, to demonstrate the use of the framework for a range of different potentially transformational technologies. Considering the context provided, the technology adoption potential is assessed for the focal regions of SSA and South & Central Asia (SCA). Each assessment considers the framework aspects of: 1. Benefit. Does the innovation improve the utilization, productivity, and/or effectiveness? 2. Application. Does the innovation apply to conveyances, guideways, and/or processes? 3. Relevance. Is the problem being addressed by the innovation relevant to the region being considered? 4. Systems. Is there sufficient support to enable innovation adoption in the region? Digitization of Freight Digitization refers to the replacement of manual or paper-based processes with digital technologies. In its most advanced form, this transforms all the manual tasks required to secure transportation capacity into automated digital processes that remove paperwork and reduce human labor (see table 4.1). In freight transportation, digitization can be applied to a wide variety of tasks including generating, uploading and sharing shipping documentation on a common web platform; providing end to end visibility of goods in transit; logging of driver hours and work; selection of preferred carrier; matching of shipments to specific carriers or drivers, and so on. https://www.un.org/ohrlls/sites/www.un.org.ohrlls/files/landlocked_developing_countries_factsheet.pdf 19 Transformative Technologies in Transportation93 Table 4.1. Assessment of Technology Innovation: Digitization of Freight Utilization Productivity Effectiveness Conveyance Guideway X – could lead to better X – add new Process X – labor reduction driver/asset utilization functionality (visibility) Node Source: World Bank. Relevance: There is great potential to improve the matching of shipper and carrier in markets where brokerage services have been very slow to emerge. Driver and asset utilization improvements offer benefits for shippers and carriers. In addition, new functionality such as visibility could make transit times more reliable. Finally, the use of mobile money to facilitate freight payment would reduce manual labor for the shipper while securely and rapidly paying the carrier. This could also accelerate adoption of mobile money, incentivizing savings and increasing access to credit. Systems: Many systems are already in place to enable rapid adoption. Mobile phone penetration is one of the highlights regarding infrastructure (communications) systems. Mobile money is already a digital backbone for the financial systems with strong potential to grow. Assessment: The potential for the rapid adoption in freight digitization is high since it addresses several relevant problems and there are no incumbent approaches. This is an opportunity for these regions to realize a transformational step change, leapfrogging the electronic data interchange (EDI) systems that are still common in developed countries. Startup companies are already moving in this direction with adaptation of several companies to incorporate freight movement during the COVID-19 pandemic (see box 4.3). Image 4.3. Futuristic Technology to Digitalize Process to  Analyzes Goods Source: Adobe Stock. Transformative Technologies in Transportation94 Box 4.3. Digital Freight Platforms Leapfrogging in Africa An ongoing challenge in freight transportation is matching the right driver and truck to the right available load. The traditional matching processes used in developing regions tend to be queueing or lining up, physical load boards, or phone calls. This leads to significant inefficiencies – wasted driver hours and trucking capacity and delay in shipments. To remedy this, several different companies have emerged to improve the matching of shipments to carriers or more specifically, drivers. While different regions have their own specific issues, most of the challenges relate to a lack of trust between the parties, law enforcement of contracts, and uncertainty in receiving payment. Companies such as Kumwe Freight in Rwanda, Lori Systems in Uganda, Kenya, and Nigeria, and Kobo360 operating in six countries in Eastern Africa are working to improve this process through the introduction of different technology platforms that better connect drivers and shippers. Kumwe Freight has developed and deployed a technology platform that connects a network of over 1,000 reliable transporters operating in Rwanda to the most agriculture-based shipping companies. Combining logistics expertise and modern technology, it attempts to drive efficiency and organization in Rwanda’s opaque domestic freight market. Lori Systems has also created digital tools to assist in the matching of carriers to shipments, and offers other services such as real-time visibility, tracking, payment, and verification. Primary benefits derive from the aggregation of data that enables better coordination between transporters and cargo owners. Challenges include enforceability of contracts, security of payments, and the congestion and lack of balance in loads – especially in those coming from ports. Kobo360 has also launched a technology platform that connects shippers and carriers digitally. Working primarily in East Africa, the system provides back-office load to driver-matching based on driver characteristics. While each of these companies might have a slightly different focus – geographically and by target industry– they all use digital technology to transform the inefficient transportation procurement process. All of these, and other startups in the region, use advanced communication formats (such as APIs) to connect the different layers. These connections are much more flexible and adaptable than older protocols such as the very rigid EDI that still dominates in the U.S. By moving directly to APIs, this region can effectively leapfrog investments in EDI and other older technologies. Automation in Freight-Handling Automation in freight handling is primarily a node innovation. It requires physical equipment as well as systems that improve the efficiency and effectiveness of the movement, put-away, retrieval, storage, loading/unloading, and/or picking operations within a facility. In general, it reduces, and sometimes eliminates, the need for manpower to perform an operation. Freight handling automation can take many forms, from robotic loading/unloading of a conveyance, to software systems that decrease the need for manual entry of processing freight shipments. The primary benefit of freight handling automation is less reliance on manual labor. Other benefits include error reduction and less freight damage (see table 4.2). There is a trade-off in that while labor costs might go down, there is a significant requirement for capital investment as well as on-going maintenance and support for sophisticated equipment. Transformative Technologies in Transportation95 Table 4.2. Assessment of Technology Innovation: Automation in Freight-Handling Utilization Productivity Effectiveness Conveyance Guideway Process X – reduces errors and Node X – labor reduction damage Source: World Bank. Relevance: The primary benefit of freight handling automation is less reliance on manual labor with secondary benefits in reducing errors and damage. There is a low incentive to invest in automation in countries where labor cost is lower, and supply is available. There is slightly higher incentive to automate data entry for processing freight transactions to reduce errors and consistently capture data that is essential for management oversight and innovation. Systems: Automation often requires 24-hour power. Given the unstable power supply in these regions, there will be greater reliance on generators and the costs of fuel and maintenance will be incremental. Due to the potential safety concerns from automated material handling equipment, the low regulation enforcement in these regions is a challenge. Assessment: Considering the factors above, there is low likelihood for adoption of automated material handling in these regions. The likelihood is slightly better for automated data entry, especially of other innovations in digitized freight accelerate. Electric Trucks EVs, while still a small percentage of the total market, are becoming increasingly popular – especially for automobiles. Electric trucks tend to lag the growth of passenger automobiles but there is significant investment in this space. The segment most suitable to electric trucks are the last-mile movements for delivery in cargo or sprinter vans. Last-mile delivery vehicles run for a relatively short distance, always return to a central facility, and tend to operate in more urban or built-up areas (see table 4.3). These are all amenable to EVs, which need consistent and reliable access to charging stations. Longer hauls for middle-mile movements are not as suited to electric trucks. Table 4.3. Assessment of Technology Innovation: Electric Trucks Utilization Productivity Effectiveness X – more energy Conveyance efficient, quieter and less polluting Guideway Process Node Source: World Bank. Transformative Technologies in Transportation96 Relevance: Electric trucks could be used in urban areas for last-mile delivery of product. Systems: Electric trucks require reliable power generation for recharging. They require a range of spare or replacement parts that are not common in developing regions. Additionally, they require a skilled labor force for repairs and maintenance. Assessment: Electric trucks have potential in some urban areas where the electricity infrastructure is stable, and power is reliable. It is not as well suited to longer haul or larger loads. Autonomous Trucking Autonomous trucks are simply trucks that do not require human drivers. They can be deployed in three general areas of transportation: yards, middle mile, and first/last mile. Autonomous trucks are used mostly in controlled areas, like yards or mining operations, where there is no external traffic to contend with. The first/last mile refers to the local driving picking up or dropping off a shipment through city or local streets. These involve a significant amount of non-controlled vehicles, pedestrians, and other external factors to contend with. Finally, the middle mile is essentially that distance between the first and last miles. It is typically on access-restricted roads with minimal entrances and exits to enable faster speeds and higher volumes. They are more predictable and easier to navigate. Autonomous vehicles are particularly compelling for long-haul tractor-trailers in the middle mile that would operate similarly to intermodal rail, moving from one access ramp to another with drivers only handling the more complex first and last mile between access ramp and origin/destination. Even if full autonomy is not realized, an “auto pilot” option where drivers could take the wheel on the middle mile if needed, would increase safety and reliability while reducing freight damage (table 4.4). Table 4.4. Assessment of Technology Innovation: Autonomous Trucking Utilization Productivity Effectiveness X – safety, reliability, Conveyance X – labor reduction damage reduction Guideway Process Node Source: World Bank. Relevance: Similar to automation in freight handling, the automation of truck driving offers low incentives in countries where labor cost is lower. The secondary benefits of safety and reliability would address common problems with truck transportation, though other investments in infrastructure and improvements in regulation of customs processes are likely to have a greater impact on safety and reliability, respectively. Systems: Road quality is consistently low in these regions. Conditions are far from the standards required for safe guidance via AV; even if road standards were dramatically improved, there is limited government capacity for the consistent maintenance required. In addition, these regions commonly use second-hand trucks due to the high investment cost and difficulty in maintaining new vehicles. Assessment: The support systems required for AVs are not in place to support adoption anytime soon. Transformative Technologies in Transportation97 Drones Drones are essentially flying robots that can be controlled remotely from the ground or fly autonomously using navigation systems, onboard sensors, and GPS. They are more formally referred to as either unmanned aerial vehicles (UAV) or unmanned aircraft systems (UAS). Drones are able to deliver small to moderate payloads to remote areas faster than surface transportation (see Box 4.4, Table 4.5). Table 4.5. Assessment of Technology Innovation: Drones Utilization Productivity Effectiveness X – reduces cost or X - able to reach Conveyance effort required to reach remote locations Guideway Process Node Source: World Bank. Relevance: Drones could address a significant gap in serving communities that currently rely on poor road infrastructure in these regions. However, this only works for certain segments of products that are low cube and weight due to payload constraints. Systems: Drones require reliable power generation for recharging. They also require appropriate governance regarding air control. Assessment: Drones offer high potential to overcome poor infrastructure and realize rapid adoption, but only for a segment of products that fit within payload limits. Fortunately, several high-priority items such as essential medicines are low cube and weight. For many product lines, the drone mode needs integration into comprehensive transportation service offerings to support a full line of products. Modern air control systems will be required for country-wide adoption in areas that overlap with previously implemented air control systems. Image 4.4. Technology logistics air cargo transportation Source: Adobe Stock. Transformative Technologies in Transportation98 Box 4.4. Drone Delivery In October 2019, UPS Flight Forward was the first to receive from the Federal Aviation Administration (FAA) a full Part 135 Standard certification to operate a drone airline in the U.S. In 2021, UPS Flight Forward announced the delivery of COVID-19 vaccines using specialized cargo boxes with a temperature-sensitive packaging mixture (Reichmann, 2021). In October 2021, Alphabet-owned drone delivery company, Wing, announced the first commercial drone delivery service in a major U.S. metro area in Frisco, Texas. These pilots are still focused on specific applications along narrow corridors, far from the robust systems serving broad geographical areas envisioned years ago. In contrast, the expansion of drone delivery systems and geographical service has been faster in African countries. Zipline worked with the Government in Rwanda to pilot operations much earlier, with the first successful blood delivery in October 2016. Within a year, the operations of their fixed-wing drones that drop off packages via parachute had scaled to 12 hospitals. Five years later, Zipline served hundreds of healthcare facilities across the entire country with a 24/7 operation, delivering 75 percent of the country’s blood supply outside of Kigali (Team Zipline, 2021). Zipline’s 2019 launch in Ghana started as a network that delivered vaccines and essential medicines in addition to blood supplies. In the four distribution centers, each house a fleet of 30 drones that fly 100 km/h to make deliveries in a 22,500 km2 area. The network serves around 2,000 health centers in remote areas that are estimated to reach 12 million people, nearly 40 percent of the country’s total population. In October 2021, five years after its first flight, Zipline reported making 210,000 commercial deliveries of over 200 different medical items to over 1,900 healthcare facilities across five countries (Vincent, 2021; Team Zipline, 2021). Generally, these pilots have shown that the value of drone delivery tends to increase with: • A higher density of health facilities within range • A difficulty level of accessing the facilities by road • The higher value of the goods (for example, financial, health, scarcity) • The greater demand unpredictability • The shorter shelf life of products Despite the proliferation of specific use cases and pilot applications, drones should not be developed in parallel with existing freight systems, but rather integrated to provide a robust, multi-modal service offering (Stokenberga and Ochoa, 2021). This World Bank study presented that the application of UAVs has shown more advanced deployment progress in certain developing countries compared with developed countries, partially due to the relatively relaxed regulatory environment. The study reviewed the costs and benefits of deploying drones across use cases in East Africa and found that UAVs have had many applications in East Africa, including medical goods deliveries, food aid delivery, land mapping and risk assessment, agriculture, and transportation and energy infrastructure inspection. Delivery based on UAVs is not cost competitive compared to traditional transportation modes under current conditions; it could become competitive in some niche market such as delivering time-sensitive medical supplies to hard-to-reach destinations. Transformative Technologies in Transportation99 Internet of Things (IoT) The IoT is a system of inter-related, internet-connected objects that are able to collect, process, and transfer data without human intervention. An example would be sensors at a facility that collects the identity of an entering vehicle and automatically transmits it directly to a transportation management system (TMS) that can then use this information to send alerts, to update schedules, and so on. Table 4.6. Assessment of Technology Innovation: Internet of Things Utilization Productivity Effectiveness Conveyance Guideway X – could lead to better X – potentially reduces Process X – labor reduction use of assets errors Node Source: World Bank. Relevance: The promise of IoT is relevant to emerging regions. Tracking of transportation assets across countries and boundaries is a long-standing problem that could be significantly improved by IoT connectivity. Having the capability of items communicating with each other could significantly improve freight operations (see table 4.6). Systems: IoT requires significant supporting systems to be successful. Broad adoption is needed to be effective. Additionally, it would require widespread broadband communication, significant advances in management science, and fundamental design of digital financial transactions. Robust deployment of sensors and equipment is challenging due to intermittent power and other infrastructure issues. There is also a concern about regulatory environment consistency across countries. Assessment: There is still no consensus as to whether the large benefits of fully-implemented IoT can be achieved due to all of the supporting system requirements. Blockchain A Blockchain is a distributed digital ledger of transactions designed to make it difficult, or impossible, to change, hack, or cheat. The idea is that a blockchain can provide a safe and scalable solution for authenticated information sharing without the need for centralized control or oversight. In freight transportation, blockchain can be used to maintain bills of lading and other documents required to move shipments across borders (see Box. 4.5, Table 4.7). Transformative Technologies in Transportation100 Table 4.7. Assessment of Technology Innovation: Blockchain Utilization Productivity Effectiveness Conveyance Guideway X – requires less X – potentially reduces Process human manual effort errors or fraud Node Source: World Bank. Relevance: Blockchain offers authentication without the need for centralized control. It could offer a potential path to formalizing aspects of the economy where financial institutions have failed. There is also the potential for more smart contracting and freight documentation in regions where contract enforcement is extremely slow and expensive. Systems: Implementation of blockchain to streamline information flows across borders requires regulatory consistency and enforcement across countries. Educational systems would need to provide a skilled workforce to implement the digital systems in regions where many businesses still lack financial literacy. Blockchain also requires massive computing capacity, which could be costly not only for computing equipment but also power generation in regions with low reliability of electricity. Assessment: The likelihood for blockchain adoption overall is low given the requirements on education and infrastructure systems. However, due to the potential relevance in overcoming legal and financial system weaknesses, pilot initiatives where actors are educated can stimulate interest. Box 4.5. Blockchain in Freight Blockchain is mostly known as an enabler of the storage and transfer of monetary value, and as the backbone of cryptocurrencies. More importantly, for freight innovation, blockchain enables transparency and security when moving data. An early financial pilot by the World Food Programme (WFP) provided a foundation for its global logistics organization to explore the potential for freight transportation processes to use blockchain. The WFP first piloted the application of blockchain technology with cash transfers in Jordanian refugee camps. The project, known as “Building Blocks,” was an Ethereum-based system where people could scan their irises to prove their identities and conduct financial transactions in supermarkets, with the transactions recorded on a private version of Ethereum. In 2018, as the pilot was effectively serving more than 100,000 refugees, WFP decided to leverage the Building Blocks identity system for a freight transportation pilot in Africa, titled “Blocks for Transport” (Stromfelt, 2020). Blocks for Transport aimed to streamline processes and reduce the time from the Port of Djibouti to the WFP warehouse in Ethiopia from the current standard of 10 to 12 days to three to five days. The current process involved 17 documents and 40-plus interactions among stakeholders. The blockchain solution combined private and shared infrastructure in providing Transformative Technologies in Transportation101 Box 4.5. Blockchain in Freight (Cont.) access to original documentation for the port and customs authorities, port inspectors and surveyors, clearing and shipping agents, transportation providers, and various WFP offices as the shipper.20 The results of this freight corridor pilot are pending. In a business context where 80 percent of blockchain initiatives in the supply chain are expected to remain at a proof-of-concept or pilot stage through 2022 (Stamford, 2020), documentation of success and failure is required to catalyze further adoption. With EDI entrenched in global trade lanes connecting developed economies, early insights from pilots in developing countries such as Blocks for Transport are worth watching. Next-Generation Air Control Guideways above ground are not as obvious as roads, rails, and waterways but they exist to ensure safety for aerial conveyance. Planes must register a flight plan and reach beacons along the aerial guideway to conform with air traffic control. Proposals for “free flight” to enable more direct routes from origin to destination could reduce time and increase the freight utilization of the airspace (see table 4.8). Drones are new and air traffic control methods are evolving to govern their safety. In some areas, especially urban areas, drones are not allowed. Where allowed, fixed-wing drones that are more effective in moving freight often register flight plans in a similar way as planes. Expanding airspace for drones could greatly expand their effectiveness. Table 4.8. Assessment of Technology Innovation: Next-Generation Air Control Utilization Productivity Effectiveness Conveyance X – reduces time in the X – drones able to Guideway air moving from origin reach urban and to destination remote locations Process Node Source: World Bank. Relevance: The potential for drones provides an opportunity for this guideway innovation. Expanding airspace for drones could address the challenge of access to urban communities, not only for delivery but also for proximity to supply, which is concentrated in cities. Systems: The civil system requirements for implementation are not as high as for other infrastructure options such as roads and rails. Early interest among government leaders in drones means that the foundational support of political systems is relatively high. There may be higher potential in smaller countries that would not require government support for control towers. https://unece.org/fileadmin/DAM/cefact/AdvancedTechnologyAdvisoryGroup/2020_1stSession/PPT_3.3_Raghu_Kiran_Nallabotula_-_WFP.pdf 20 Transformative Technologies in Transportation102 Assessment: The likelihood of adoption is medium, depending on the adoption of drone conveyance and the infrastructure system requirements for a large country. Advanced Train Control System (ATCS) The ATCS provides a digital train management solution that focuses on real-time train monitoring, seamless information sharing between drivers and control centers, and automatic situation recognition and awareness. The system will improve the safety, network reliability and productivity of the rail network by ensuring safe and on-time operations (see Table 4.9). For example, ATCS is part of the intermodal and rail development project in Tanzania, which is expected to renovate the existing rail lines and significantly increase the transportation capacity. Table 4.9. Assessment of Technology Innovation: Advanced Train Control System Utilization Productivity Effectiveness X - Improves the X – Improves the X – Enhances the capacity and Conveyance efficiency by reducing safety productivity of the delays railway network Guideway Process Node Source: World Bank. Relevance: The adoption potential for the ATCS is large as it offers the opportunity to upgrade the conventional railway control and bring the system to the digital age. The automatic train monitoring and communication enhances the safety and productivity at both the drivers’ and the control centers’ end. Systems: The ATCS has been implemented in many developed countries to some extent. Additional opportunities exist to further enhance the system and realize its full potential. The implementation of such a system requires the deployment of supporting infrastructure such as the ICT system, which is usually part of an integrated system development. Such improvement usually benefits not only the railway operators, but also port operators, freight-forwarding agencies, shippers, importers, and exporters, as the rail operation becomes more efficient, reliable, and transparent. Assessment: The likelihood of adoption is high as a popular upgrade option for the railway network in developing countries. Zero-Carbon Bunker Fuels - Green Ammonia Powered Ships The maritime transportation sector needs to abandon the use of fossil-based bunker fuels and turn toward zero-carbon alternatives that emit zero or very low GHG throughout their lifecycles. The maritime industry is actively looking for its solutions for carbon reduction, and green ammonia is a promising candidate for zero-carbon bunker fuels. A World Bank study (Englert et al., 2021) shows that green ammonia, produced from renewable electricity, is more advantageous than other candidates such as hydrogen and biofuels when factors such as the lifecycle GHG emissions, other environmental factors, scalability, economic viability, and safety are considered (see Table 4.10). Transformative Technologies in Transportation103 Existing fleets can be upgraded with minimal modification to burn ammonia, an important factor for economic feasibility. As technology advances, ammonia can also be used with fuel cells technology, which is a more preferable option for the long term. Table 4.10. Assessment of Technology Innovation: Zero-Carbon Bunker Fuels - Green Ammonia- powered Ships Utilization Productivity Effectiveness X – Reduces GHG Conveyance emissions and other pollutants Guideway Process Node Source: World Bank. Relevance: The potential for ammonia-powered vessels in the maritime industry is huge. It offers significant environmental benefits and would also be cost effective as its production costs continue to drop. In the near term, it offers a viable fuel pathway towards greener maritime transport, although investment in both infrastructure and technology development is needed to achieve long- term goals. Systems: The adoption of green ammonia-powered vessels requires the enabling infrastructure for the production and storage of ammonia. It also requires the adaption of existing fleet and in the long term, the development of fuel cell technologies for ammonia. Assessment: The likelihood of adoption of ammonia-powered ships is medium, as green ammonia is not cost competitive yet and safety concerns around its toxicity still need to be addressed. Policy Implications As technology evolves, there will continue to be opportunities for transformative innovation in freight transportation. Beyond the technical or scientific discovery alone, technology needs to be adopted widely and shift the paradigm from prevailing practices to be transformative. Policy makers in developing countries can use the framework provided in this chapter to understand freight transportation opportunities and invest constrained resources wisely to support the technologies with the most potential for adoption. Apply Systematic Assessment to Evaluate Technology Adoption Potential in Developing Countries For a technology to be widely adopted, it needs to have sufficient direct or indirect benefits. and in the freight sector, it needs to improve system performance in terms of utilization, productivity, and effectiveness, arising from innovations from the conveyances, the guideways, nodes, or the processes. Innovations can come both in a bottom-up and a top-down paradigm and the government should be open to both models. The public sector can assess the potential of a technology adoption by answering four questions: How does this innovation improve the utilization, productivity, and effectiveness of freight transport? Where does the innovation improve a transportation system Transformative Technologies in Transportation104 in terms of its conveyances, guideways, and processes? Is the problem being addressed by the innovation relevant to the specific region? And is there sufficient support to enable innovation adoption in the region in terms of political, social, legal, and others? The first two questions characterize the potential innovations, and the latter two questions assess the adoption potential under a developing country context by pointing out that the innovation needs to be relevant to the region, to address problems, or realize opportunities that exist in the specific region. Meanwhile, the supporting systems need to be able to encourage innovation adoption in the region, including in financial, legal, political, educational, and civil infrastructure development. This assessment framework can assist in policy recommendations to improve the overall performance of the freight transportation system. Embrace the Local Context for Technology Adoption It is important to recognize that some technologies being pursued in other regions may be poor bets for wide adoption in developing countries due to factors such as infrastructure, rule of law, regional regulatory consistency, access to finance, and professional education. The assessment above demonstrates how to use the framework to assess opportunities in a region or country. On the other hand, contextual factors may provide opportunities for some technologies to advance faster in developing countries, leapfrogging the dominant paradigms that have become entrenched in other regions. For example, digital freight platforms can leverage the wide adoption of phones for business transactions to leapfrog traditional communications standards like the rigid EDI that still dominates in the U.S., to leverage much more flexible and adaptable protocols. The key is identifying technology and innovation that is ‘adjacent possible’ for that area. This means that there are relevant problems and supporting systems, institutions, and infrastructure to motivate widespread adoption and a shift in paradigm. Foster Innovation Through Experimentation and Partnerships Transformational innovation tends to be a combinatorial process rather than a singular breakthrough. It is usually built up from a combination of minor previous innovations into something that can be transformational. Innovation tends to move in adjacent steps, building from one advance to another over time as people, companies, and organizations experiment and subject it to trial and error. Essentially, innovation is more of an iterative process than a lightning strike moment. Public and private sector experimentation on the edges of possibility is required to identify the right combinations for innovation. The creation of “living labs” can accelerate such experimentation (Schumacher and Feurstein, 2007; Quak et al., 2016). Adoption of drone delivery technology happened much faster in SSA because of early public-private partnerships in Rwanda and Ghana and a drone-testing corridor in Malawi. Government support on rapid experimentation with airspace infrastructure is enabling vital deliveries to overcome poor road infrastructure and reach remote locations quickly and safely. Align Incentives Across Public and Private Sectors Assembling and supporting adjacent innovations to cultivate transformative paradigm requires experimentation and risk taking for individuals, companies, and organizations to advance via trial and error. The private sector’s primary contribution will be creatively connecting innovations with relevant problems. Their incentives are direct financial benefits such as cost reduction, revenue Transformative Technologies in Transportation105 growth, asset productivity that, for the freight sector, cannot be too far into the future. The public sector’s primary contribution is consistently providing the financial, legal, political, educational, and civil support systems. Their incentives are indirect benefits that might be transformative in the long term, such as increased trade, more equitable access to goods, economic development, better resilience to shocks, lower environmental impact. Alignment of short- and long-term incentives across public and private sectors is required to motivate the adjacent steps on a path to widespread adoption and transformative innovation. References Arvis, J. F., L. Ojala, C. Wiederer, B. Shepherd, A. Raj, K. Dairabayeva, and T. Kiiski. 2018. Connecting to Compete 2018: Trade Logistics in the Global Economy. Washington, DC: World Bank. Berger, Paul. 2021. “Port of Los Angeles Stops Short of 24-Hour Operations, Unlike Long Beach.” Wall Street Journal. September 24, 2021. Beyer, S. 2021. “Hyperloop’s Potential for Revolutionizing Freight.” Catalyst. https://catalyst. independent.org/2021/07/23/hyperloops-revolutionizing-freight/ Caplice, Christopher George. 1996. “An Optimization Based Bidding Process: A New Framework for Shipper-Carrier Relationships.” Unpublished dissertation, Massachusetts Institute of Technology. Caplice, Christopher George and Y. Sheffi. 1994. “A Review and Evaluation of Logistics Metrics.” The International Journal of Logistics Management 5 (2): 11–28. Department of Transportation. 2020. “Pocket Guide to Large Truck and Bus Statistics.” Federal Motor Carrier Safety Administration. Dumitrescu, A., B. El-Hifnawi, and L. Zemke. 2018. How Can LLDCs Overcome the Plight of Land Locked-ness? Transport and Digital Development Snapshots. Englert, D., A. Losos, C. Raucci, and T. Smith. 2021. The Potential of Zero-Carbon Bunker Fuels in Developing Countries. Washington, DC: World Bank. http://hdl.handle.net/10986/35435. Vincent, J. 2021. “Self-flying Drones Are Helping Speed Deliveries of COVID-19 Vaccines in Ghana.” https://www.theverge.com/2021/3/9/22320965/drone-delivery-vaccine-ghana-zipline-cold-chain- storage. McKinnon, A., C. Flöthmann, K. Hoberg, and C. Busch. 2017. Logistics Competencies, Skills, and Training: A Global Overview. Washington, DC: World Bank. Montreuil, B. 2011. “Towards a Physical Internet: Meeting the Global Logistics Sustainability Grand Challenge,” CIRRELT 2011-03. https://www.cirrelt.ca/documentstravail/cirrelt-2011-03.pdf. Ndulu, B. J. 2007. Challenges of African Growth: Opportunities, Constraints, and Strategic Directions. Washington, DC: World Bank. Quak, H., M. Lindholm, L. Tavasszy, and M. Browne. 2016. “From Freight Partnerships to City Logistics Living Labs—Giving Meaning to the Elusive Concept of Living Labs. Transportation Research Procedia 12: 461–73. Ratcliffe, S. 2016. “Roy Amara 1925–2007, American futurologist.” Oxford Essential Quotations. 1 (4th ed.). Oxford University Press. Transformative Technologies in Transportation106 Reichmann, K. 2021. “UPS Begins Delivering COVID-19 Vaccines with Drones in North Carolina.” https://www.aviationtoday.com/2021/08/26/ups-begins-delivering-covid-19-vaccines-drones- north-carolina/. Stokenberga, A., and M. C. Ochoa. 2021. Unlocking the Lower Skies: The Costs and Benefits of Deploying Drones across Use Cases in East Africa. Washington, DC: World Bank. Schumacher, A. J. and B. K. Feurstein. 2007. “Living Labs—A New Multi-Stakeholder Approach to User Integration. In Enterprise interoperability II (pp. 281–85). London, UK: Springer. Stamford, C. 2020. “Gartner Says 80% of Supply Chain Blockchain Initiatives Will Remain at a Pilot Stage through 2022.” https://www.gartner.com/en/newsroom/press-releases/2020-01-23- gartner-says-80--of-supply-chain-blockchain-initiativ. Stromfelt, G. 2020. “The Atrium: UN Blockchain Solutions under One Roof.” https://wfpinnovation. medium.com/welcome-to-the-atrium-7c4182d3682d. Team Zipline. 2021. “Zipline Turns Five: A Look Back, and Ahead.” https://blog.flyzipline.com/zipline- turns-five-a-look-back-and-ahead-760a3fd8b20. Enabling Policy Support for Technology Adoption - Connected Institutions 5 Public institutions can influence the adoption of transformative technologies in the transportation sector By establishing enabling regulatory frameworks, fostering innovation-friendly environments, and providing targeted financial incentives, governments can create the necessary conditions for technology adoption to thrive. Equity • Consider distribution of access Economy Efficiency spatially and by socio-economic • Transport investment with a or demographic group high rate of return measured in • Evaluate comparative cost faced land value appreciation by user groups for the same Negative • Maximize access and enable service or access level Externalities valuable activities for wealth • Measure equity using accessibility generation and job opportunities for different • Address unintended outcomes like congestion, • Reinvest tax revenues from population subgroups User Experience increased activity and land value noise, crash risk, pollution, • Include attributes related and emissions to ride comfort and user safety in transport system evaluations The 4Es Framework Promotional Regulatory Persuasive and example-setting Regulation and enforcement mechanisms to approaches to encourage desired shape urban form and vehicle technology behaviors, often deemed ineffective in involve implementing rules and regulations the transport community to govern behavior in the transport sector Public Policy Financial Strategies Platform Subsidy, finance, investment, contracting, Establishing technological standards to and taxation methods to support policy guide deployment at scale, establish objectives involve raising and spending standards in the transport sector, like GTFS money through subsidies, tolls, and for transit data and GBFS for bikeshare data congestion pricing Collaborative Decision Making (CDM) to Transform Transportation CDM manages shared resources among multiple entities Perks Challenges • Greater coordination for effective services • Inter-jurisdictional cooperation challenges • Ensures data privacy and cybersecurity in new models • Willingness to share critical data • Seamless urban mobility solutions with MaaS • Avoiding discrepancies and redundant data • Multimodal systems to manage AVs and micromobility • Balancing sensitive data in private sector involvement • Real-time freight marketplaces • Reconciling different data sources across entities Policy Implications Embracing open data for public benefit Driving innovation through standardization and leveraging platform power Learning from success and failure to accelerate future innovations Transformative Technologies in Transportation109 Chapter at a Glance In transportation, every country is developed, and every country is developing. It is imperative for developing countries to seize emerging opportunities to leapfrog the traditional transportation development processes. This chapter explores how public institutions can influence the development, deployment, and adoption of transformative technologies in the transportation sector. It emphasizes the importance of leveraging policy strategies to achieve positive dividends in the transportation system, including enhancing economic efficiency, promoting equity, mitigating negative externalities, and improving user experiences, in connection with the implementation of transformative technologies. The chapter highlights the power of various policy strategies of promotional, regulatory, financial, and platform strategies. Additionally, it illustrates the concept of connected institutions, which effectively enable enhanced capabilities in managing technology-heavy systems. It also presents the opportunities made possible through collaborative decision-making structures, augmented by real-time data streams and AI algorithms, to foster information sharing and human interaction in these emerging contexts. Transformative Technologies in Transportation110 Context The transportation sector encountered huge technological and social changes. Collectively, this has been described as the three revolutions: automation, sharing, and electrification (Sperling, 2018). The primary technology drivers affecting the sector are discussed throughout this report. While several are well underway in terms of deployment (for example, mobile apps, ITS), others are still in the early stages. Realizing their potential and guiding deployment towards sustainable, equitable, and socially desirable outcomes in both developed and developing countries call for thoughtful planning and policy responses by the organizations and institutions entrusted with transportation and mobility at local, state, and national levels. Key challenges arise from the fragmented ecosystem, the uncertainties inherent in the development of disruptive technologies, and the inherent risks associated with the disruption of a large-scale system that has evolved over decades, even centuries. Specifically, connectivity enables greater visibility and information sharing across jurisdictions, modes, and sectors. Thus, it can thus support collaborative approaches to decision-making and policy implementation, better integration across modes and services, and greater responsiveness to connected travelers. Similarly, automation enables greater reliance on AI and machine learning in managing transportation systems, especially for the operational aspects of both supply and demand management. Electrification and decarbonization become more readily attainable when the electric grid and utilities are better connected with transportation organizations, enabling multi-sectoral collaboration and complementarity. New models of public-private cooperation would also contribute to achieving the granularity and efficiency enabled by sharing economy models, while mitigating the potential pitfalls of chaotic fragmentation. Therefore, the direct and indirect opportunities introduced by transformative technologies on the operation and performance of transportation institutions in the delivery of mobility services need to be examined. In addition to the usual policy strategies of funds and enforcement, the first section of the chapter highlights the role of standards and platforms in shaping the development and deployment processes. It addresses how policies (regulation, subsidy and taxation, finance and investment, technological standards, research and development) regarding transformative technologies can change the transportation system to increase equity, reduce negative externalities, improve user experience, and enhance economic efficiency. It formulates a framework that maps policy tools to specific aims, provides examples to illustrate it, and attempts to develop general principles about what strategies are most likely to be successful, and what the conditions of those successes are. There is special focus on technological standards because of their potential role in the deployment of transformative technologies, helping to foster innovation as well as control excesses. Public Policy Objectives Public policy often aims to provide economic efficiency, improve social equity, reduce externalities, and ensure a positive user experience. These four ‘Es’ form a framework for evaluation (Levinson and Krizek, 2007). Economic efficiency can be operationalized several different ways. Investment in transportation is necessary, especially in the early stages of economic development, and has a high rate of return, which can be measured in land value appreciation. This land value can recover the cost of the investment if it is captured by the infrastructure provider, which can further grow the economy. Transformative Technologies in Transportation111 Public policy should be designed to use investment in a way that maximizes access and enables valuable activities that generate wealth. The tax revenues from the increased activity and land value should be devoted to reinvesting in the system (Levinson and Istrate, 2011). Equity can consider the distribution of access across the population spatially or by socio- economic or demographic group (El-Geneidy et al., 2016, Foth, Manaugh, and El-Geneidy, 2013). Equity could also be defined as the comparative cost that each user group faces for the same service (or level of access) in comparison to the overall average or other groups. Studies have used accessibility to measure equity, often comparing the share of types of jobs reachable by a subgroup (for example, the lower-income quartile) of the population with the whole population (Palmateer, 2018; Yeganeh et al., 2018). Negative externalities describe outcomes of economic transactions, such as taking a trip, that are not the intended outputs. At the individual level, it may include congestion, noise, crash risk, environmental pollution, and carbon emissions. User experience is the collection of attributes describing how people perceive and experience the system. For instance, ride comfort or user safety does not appear in conventional efficiency or equity analyses in transport, which tends to prioritize time and money. The 4 Es are a useful way of organizing the outcomes anticipated from transportation policies and projects. All can be considered in the access framework. From a social perspective, access depends not only on the user’s individual time and cost, but also on the social costs required to enable that access. Thus, infrastructure expenditures and negative externalities such as pollution, emissions, and crashes enter into a full cost access analysis (Cui and Levinson, 2018, 2019). Public Policy Strategies Strategies to achieve public policy aims employ the following types of powers: • Promotional strategies: persuasive, exhortation and example setting • Regulatory strategies: regulation and enforcement • Financial strategies: subsidy, finance, investment, contracting, and taxation • Platform strategies: establishing technological standards The strategies of governmental regulation are an important shaper of urban form (Ben-Joseph and Szold, 2005), and also affect vehicle technology. However, it has been limited in the past decade for new technologies such as • Google’s initial test of AVs in California (Markoff, 2010) • Uber’s introduction of ride-hailing services (Crespo, 2016) • Tesla’s 2015 launch of Autopilot, allowing hands-off driving (Nelson, 2015) Transformative Technologies in Transportation112 Standards play an important role in the process of technology deployment at scale. Many technological standards are adopted by society without being formulated by the government. Some standards are promoted through official bodies but not widely adopted. Some others get established because of government intervention, not only through law, but because the government is a major producer and consumer itself. Adopting or developing technological standards constitute platform powers, and is a tool for the public sector. Table 5.1 shows how policy objectives and instruments interact, sorted by illustrative applications (successes and failures). This matrix framework has general applicability to essentially any policy instrument and context. Examples are discussed next with a focus on selected emerging transformative transportation technology platforms. Image 5.1. Starship robots autonomous delivery Source: Starship Technologies. Transformative Technologies in Transportation113 Table 5.1. Public Policy Strategies Framework Sorted by Illustrative Applications Policy Objectives Public Policy Strategy Efficiency Equity Environment Experience Promotional Promotional Variable message Change Clear the Air Buckle-up Strategies signs, encourage operator days Campaigns carpooling behavior towards special-need populations Regulatory Regulation Anti-trust Ride hailing International Dockless Strategies (regulation to market in used bikesharing, protect users) vehicles airport security Subsidy Railroad land Public Bike sharing, Public-funded grants transportation EVs, police serving fares intercity rail private electrification modes Financial Tax and Congestion charge Lifeline transit Carbon tax Congestion Strategies Tolling passes tolls Finance and Asset recycling Public port Public EV New airport Investment subsidies charging construction stations Technological National ITS Standards EV charging Transit Standards architecture; curb of accessible stations (GTFS), standards, ride infrastructure standards bikesharing hailing, traffic for people with (GBFS) signal standards; disabilities (for Platform traffic count and example, ADA Strategies speed data, real- standards) time tolls and road prices Research and AVs AVs for EVs and AVs Development people with battery disabilities technology Source: World Bank. Promotional Strategies The general feeling in the transportation community is that promotional strategies are largely ineffective, and may be counterproductive, for example, by creating a false sense of accomplishment that diverts attention from actual lasting measures. Malik, Circella, and Alemi (2019) examined “Bike Month” in Sacramento, which aimed at increasing bicycle use and improving the experience for bicyclists due to the safety effect but found no lasting impact. Longer-term programs that make real changes to the physical environment and present that in a sustained way to travelers, such as ‘Safe Routes to School’ programs, are more likely to have lasting impacts (Boarnet et al., 2005). Transformative Technologies in Transportation114 Another example is “Clear the Air” days, aimed at reducing the negative externalities of air pollution by reducing travel and other causes of air pollution on days with particularly bad weather conditions. An evaluation of the program in Salt Lake City found it largely ineffectual (Teague, Zick, and Smith, 2015). Based on these, those trying to effectively change to improve social welfare have sought more impactful measures. Regulatory Strategies Regulatory strategies, the implementation of regulations of traveler or organizational behavior by governments, backed by the government’s use of force, are important in many aspects in transport. They include rules and regulations that drivers must obey on public streets, and the rules governing the ownership of vehicles. Two examples are discussed below: the international market in used vehicles and parts, and dockless bike sharing in China. The International Market in Used Vehicles and Parts With rising EV market shares, the issue of what happens to now obsolete second-hand ICE cars gains in significance. UNEP (2020) reports that “the three largest exporters of used vehicles, the European Union (EU), Japan, and the U.S., exported 14 million used light-duty vehicles (LDVs) worldwide between 2015 and 2018. The major destinations for used vehicles from the EU are West and North Africa; Japan exports mainly to Asia and East and Southern Africa, and the U.S. mainly to the Middle East and Central America.” This runs counter to achieving environmental goals, particularly reducing the negative externality of air pollution and GHG emissions. Used cars, when they were manufactured, were subject to weaker environmental regulations than the more recently manufactured vehicles, and any environmental mitigation technologies those vehicles may have employed would have worn out (or been removed) and become less effective over time. A Dutch study finds “over 80 percent of the used vehicles currently exported to West African countries will soon no longer be acceptable due to stricter environmental regulations of the recipient countries in West Africa” (HTEI, 2020). Further, by being in countries with laxer environmental standards, and in poorer countries where used cars are maintained for even longer, the environmental impacts are exacerbated. There are also safety issues; older cars had to adhere to less rigorous safety standards, and the safety features may become less effective as the car ages and experiences wear and tear. A report recommended the development of regulations in the trade of used vehicles to impose environmental and safety standards (UNEP, 2020). The consequence of additional environmental regulations is that the supply of vehicles to importer nations will drop (as some vehicles will inevitably fail the test and cost too much to retrofit) and the costs of the remaining used vehicles will increase, reducing the number of people who can afford auto-mobility, and potentially reducing accessibility and economic activity. Further, a newer used vehicle being imported might be environmentally less consequential than an existing used vehicle on the road, which might be retired if a replacement were available at a low enough price. Another strategy for reducing the size of this problem is ‘cash for clunker’ or ‘scrappage scheme’ programs, which help buy out old, still-operating, heavily polluting vehicles and recycles the materials rather than reuse the vehicle by moving it to the used vehicle market. The effects of these are, at best, mixed (Van Wee, De Jong, and Nijland, 2011), particularly considering the energy (and emissions) required for new car production. Transformative Technologies in Transportation115 A related issue likely to emerge over the next decade will be what to do with used EV batteries (SSATP PPIAF, 2021). Older, less efficient batteries may be downcycled and used for solar storage or exported. As with end-of-life vehicles, dead batteries will have to get managed, and ideally recycled in an environmentally acceptable manner, with their parts and minerals recovered for reuse, which is especially important to avoid the negative impacts of mining. Dockless Bike-Sharing in China Bike-sharing can be used both to connect people with their destinations directly, and as a solution to the first/last-mile problem, enabling people to reach longer distance public transportation lines, increasing the efficiency of public transport. Station-based bike-sharing, which was deployed in many cities in the early 2010s mostly served the point-to-point travel market, as bikes had to be collected from and parked at stations. Enabled by smartphones, GPS, and soon-to-be-inexpensive batteries, dockless bike-sharing took off in China in the second half of 2016 ( Little, 2017), and has spread to cities around the world. Due to its locational flexibility, dockless bike-sharing was able to serve more markets than station-based sharing, and became especially popular as a first/last-mile solution. Evidence shows that bike-sharing increased passengers on subway lines (Fan and Zheng, 2020; Guo et al., 2021). By 2017, 15 bike-sharing companies served Beijing (Li, 2017), and peaked at 2.35 million bikes. There was a perception of over-supply (as most bikes were not in use at most times). In response, the Beijing Municipal Government began to regulate the entrance of bike-sharing companies and the total number of shared bicycles. As a result, the number of shared bikes in Beijing has fallen to around 900,000 units (Zhang, 2020). New institutions need to be formed to manage the system. Bike dispatchers around major destinations organize bikes. The dispatchers are employed by the bikeshare companies because of Government regulations (Qiu, n.d.). The number of dispatchers has increased markedly since 2017. The Government recognized that disorderly-parked shared bikes caused severe social problems, including blocking pedestrian paths and bike lanes. Bikes work better when infrastructure is in place, as in many Chinese cities with their already extensive network of high-quality bike lanes, a legacy in many ways of older urban design in the era before widespread motorization and the expansion of Metros. Notably, the success of dockless bike-sharing could not be repeated in cities like Sydney without such lanes (Heymes, 2019). New technologies and service models often bring about a governmental response. The opportunity for these technologies to flourish partly depends on whether the prevailing regulatory environment tilts toward “all is permitted except that which is prohibited” or “all is prohibited except that which is permitted”. Financial Strategies Financial strategies involve raising and spending money on the part of government, and are probably the most common way that governments bring about change, by subsidizing the deployment of infrastructure or service. Charging for use of infrastructure (for example, road tolls) is an example of financial instrument (coupled with enforcement). Congestion pricing has, however, been politically difficult to implement in most cities, with only a handful of well-documented examples, such as Singapore (Theseira, 2020). An example of financial instrument for road tolling is discussed in Box 5.1. Transformative Technologies in Transportation116 Box 5.1. Dynamic Toll Lanes Offers Social and Environmental Benefits Over the past few decades, policy makers have discussed the possibility of congestion pricing and using tolling for social and environmental benefits. In reality, most toll roads across the world are based on static tolls and the potential for congestion mitigation and environmental improvement remain largely unexplored. Beyond the political barrier (people usually consider congestion pricing as another tax), this lack of real-world implementation is largely due to technological constraints such as the lack of real-time and accurate traffic information for calculating the socially optimal toll levels, and hefty transaction costs. In the developed world, the deployment of various ITS is closing the data gap rapidly, while the increasing penetration of the electronic toll collection (ETC) system helps to reduce the transaction costs. Over the past decades, the U.S. has led in the deployment of dynamic toll strategies. It is mainly in the form of dynamic toll lanes, where the price is calculated in real time based on the level of usage (for example, I-66 Express Toll lanes in Virginia, I-635 LBJ TEXpress in Texas). Dynamic toll lanes in the U.S. usually give a price advantage to HOVs and green vehicles to further expand the social benefits. The Government also addresses the equity issues by offering access credits to low-income households and free access to the public transit. A study by Cetin et al. (2021) shows a wide range of people benefited from the dynamic toll lanes. Lessons learned in the U.S. could inform other countries that aspire to leverage the “purse power” and encourage more socially and environmentally preferable travel patterns through tolling. Platform Strategies Platforms like research and development and technological standards can play an important role in directing long-term change, both positively (such as by enabling compatibility across providers) and negatively (such as by stifling innovation). Examples are provided for both failed and successful standards for various technologies and programs, as well as the areas in which standards may still be emerging. Failure to Launch – U.S. National ITS Architecture In the early 1990s, the U.S. Government supported the development and deployment of Intelligent Transportation Systems (ITS). A National ITS Architecture was launched to provide a standard national interoperable ITS structure. Similar ITS programs were emerging in Japan and Europe as well. One project of this initiative, the National Automated Highway System Consortium’s (NAHSC’s) DEMO ’97 demonstrated an Automated Highway System (AHS) on the reversible High Occupancy Vehicle (HOV) express lanes of I-15 in San Diego, CA. Image 5.2 shows specially equipped cars following closely (6.4 m) at high speeds without driver intervention using vehicle-to-vehicle communications, along with in-road magnets for lane guidance. Despite its technical success, a way forward towards implementation could not be readily identified and it was discontinued. Transformative Technologies in Transportation117 Image 5.2. Demo of Automated Highway System, 1997 Source: National Automated Highway Systems Consortium, California PATH. Connected Vehicles has a steep ‘chicken-and-egg’ deployment problem to resolve: who will invest in the network without the vehicles to take advantage of them? Who will buy vehicles without the network being ready? Vehicles are manufactured by firms that have no direct influence on the state of the road network, while streets and highways are provided by public sectors with almost no influence on the designs marketed by vehicle makers. Service providers (for example, drivers, taxis, buses, freight) have almost no influence on either, aside from choosing which vehicle to purchase. The most recent advances in the transportation sector seem to come from new entrants (for example, mobile phones and wireless communications, Google, Waze, Uber, Tesla, and numerous ‘new mobility’ firms) rather than traditional players (departments of transportation, legacy automakers). The alternative approaches, illustrated with several examples in this chapter, allows the standards for technologies to emerge, and then aims to achieve compatibility and standardization among successful systems. Successful Standards Two examples of standard success stories that emerged in the transportation domain in the past decade are transit data and bikeshare data. Both are data standards that have enabled considerable additional capabilities for customers, agencies, and application developers. Transit Data - General Transit Feed Specification (GTFS) GTFS is the underlying standard that describes each agency’s public transportation schedules to software that helps prospective transit users choose routes. It emerged organically from 2005 when Google Maps engineer, Chris Harrelson, collaborated with data managers at Portland’s public transit agency, TriMet, to export their schedule in a format that Google maps could import. This proof-of-concept service was expanded beyond Portland, and each additional city adopted the same standard export format, then called the Google Transit Feed Specification (Baskin, 2018, McHugh, Transformative Technologies in Transportation118 2013). Initially GTFS described the transit system in a static way, based on the pre-established timetables. This alone has significantly improved the efficiency and user experience of transit passengers. GTFS further enabled the measurement of transit accessibility across the U.S. and many cities across the globe (Wu, Levinson, and Sarkar, 2019; Wu et al., 2021). Coupled with real-time automatic vehicle location (AVL) data, a real-time GTFS format (GTFS-RT) emerged; it specifies where vehicles actually are on the network at every moment, and allows software to project when they will reach future stops and stations. Similarly, vehicle occupancy, important from a user experience perspective, but in times of social distancing, important for public health as well, can also be shared in real-time. Generally, the local transit agency provides GTFS data. The challenges in developing countries are more pressing. Issues associated with informal transportation include irregularity of schedules and routes, lack of knowledge or interest in publicizing these schedules (as the routes and drivers may already be at capacity and the local market well-understood), and no easy way for an individual to digitize and post their schedule in a GTFS format even if they wanted to. The organization, WhereIsMyTransport, is developing GTFS standard schedules in emerging market cities, for both formal and informal public transport, supporting analyses by the World Bank (Peralta-Quiros, Kerzhner, and Avner, 2019). This extends the work of the “Digital Matutus” project in Nairobi, which documented the informal transportation sector and created standardized GTFS representations of the services (Klopp et al., 2015, Williams et al., 2015). There are further proposals to extend the GTFS standard, (for example, to include on-demand transit services), which would be especially valuable in developing countries. A key to a successful standard is not that it is complete initially, but that it is flexible and extensible and can be adapted to address new issues as they arise. More sophisticated standards for representing transit schedule information, including the European-based NeTEx and the UK’s TransXChange, have not been as widely adopted. Establishing this standard has had broad and unanticipated effects, and points the way to additional standards for better quantifying the transportation system. Bike-sharing - General Bikeshare Feed Specification (GBFS) The digitally enabled new mobility (collectively, car-sharing, bike-sharing, and shared micromobility, ride-hailing, and other services that started to expand rapidly during the 2010s) has fostered a new thinking about standards. Some shared bike and scooter companies have made their data available to government agencies and researchers. The real-time GBFS standard, modeled on GTFS, now used by more than 450 operators, was first drafted by Mitch Vars of Minnesota’s NiceRide bikesharing System in 2014, and adopted by the North American Bikeshare Association (NABSA) in 2015, with fast vendor uptake. It contains data about the location of bikes and stations, bike and dock availability, station status, and business rules. The GBFS standard is maintained by MobilityData on behalf of NABSA, and has been updated to account for dockless bikes. This standard allows integration with maps, and may facilitate various MaaS applications. This illustrates how standards like GTFS can engender more standards, building innovations in an incremental fashion. Emerging Standards Following the model of GTFS, there are emerging standards in several fields, but these are not without difficulties. Three relevant examples are given in this section regarding micro mobility and ride-hailing data (mobility data specification [MDS]), traffic signals, as well as parking availability. Transformative Technologies in Transportation119 Micromobility and Ridehailing - Mobility Data Specification In response to the rise of micromobility, Los Angeles established a MDS (Carey, 2020). The aim was to create a ‘digital twin’, a definitive digital data model that represents reality as precisely as possible, including the spatial-temporal position of every user and vehicle (Bliss, 2019). While initially the MDS aimed to track micromobility vehicles, it was soon adapted to ride-hailing vehicles, such as those of Uber and Lyft, especially out of concern for a future with zero-occupancy automated vehicles, empty Ubers cruising for passengers. The standard has been extended to cover bike-sharing and other services. In the absence of real-time information, shared dockless devices will be left in undesirable places, or block sidewalks. The MDS is now maintained by the Open Mobility Foundation (Bliss, 2019), with representation from multiple cities. There are three primary applications for the MDS data (Webb, 2020a): planning, regulation of providers, and enforcement of traffic laws for public safety. Each application has distinct data requirements and legal constraints. One of the primary flashpoints is protecting user privacy and freedom of movement vs. real-time tracking of user information (Bliss, 2019). Traffic Signals – Management Information Bases (MIBs) The traffic signal controller standards have MIBs (NTCIP Joint Committee, 2021). MIBs are human and computer-readable object definitions used by system integrators, equipment manufacturers, and agencies telling controller software and hardware how to control the traffic signal. The intent was that equipment from different manufacturers would be interoperable, preventing technological lock-in. This is especially important in the traffic signal sector, where equipment may be in the field for decades and updated rarely. Unfortunately, the NTCIP standards have not kept up with changes in technology, so vendors have created manufacturer specific MIBs for new features. This means new equipment from one vendor is incompatible with new equipment from a different vendor. However, even if that level of standardization is established and all the controller units are updated with the new standard in a timely manner, there are other issues in the traffic signal sector. It would be useful to know at any given time, the state of each traffic signal (red, amber, green). This would allow short-term navigation to take advantage of the knowledge of road speeds and timings to navigate a route through a city using a minimal amount of time and energy. The Audi Green Light Optimized Speed Advisory (GLOSA) system can retrieve data from 25,000 compatible intersections in more than 25 cities (Autovision News, 2020, Hawkins, 2019), in partnership with data providers. However, this is a pilot project in an early stage of deployment, and if most metropolitan traffic signal systems adopt the standard and share their data, it could be extremely valuable in pacing and platooning traffic in a way that minimizes stopped time at intersections. Yet, to date, these data are not open in the way GTFS is, meaning, analysts cannot just download it. Some traffic signal operators see monetization possibilities from releasing the information, so subscribers to services like Audi’s will pay a premium to get this information and others will not. This raises equity concerns, but also significant efficiency concerns, as the system works best if everyone has this data available, not if it is apportioned to selected vehicles. Parking Data System A comprehensive parking data system would track parking price, availability, and utilization for both on-street and off-street systems. Some cities with municipal parking facilities have provided this for years (For example, St. Paul, Minnesota). Park-and-ride has long been a way for travelers to connect with rail and bus services. This kind of data is becoming integrated with major mapping Transformative Technologies in Transportation120 services (Hawkins, 2021). However, there is no standard representation of parking in multiple cities, and obtaining this data remains an issue. It is particularly challenging with private actors controlling parking supply in many places, as the occupancy data is perceived as commercially sensitive. Prices are, however, posted on different platforms, including SpotHero and Parkopedia, while other services focus on managing and payment for parking, like Passport Labs, and ParkMobile in the North American market, or Divvy in Australia. An Alliance for Parking Data Standards (APDS),21 led by the UK DoT, are promoting an open parking data standard with participation from many, but not all, key players in the field. A special application area addresses overnight parking for trucks at rest areas. There are vertical apps (Trucker Path, TruckAvenue) for truckers that feature parking spaces, importing data from systems such as Florida DoT’s Truck Parking Availability System (TPAS) or the Mid-America Association of State Transportation Officials’ (MAASTO’s) Truck Parking Information Management Systems (TPIMS) in Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Ohio and Wisconsin, but the data are not complete and are not yet surfaced into a standard maps application. In summary, standards are important for transforming transportation and speeding delivery of new mobility services, electrification, automation, and other advancements. Standards can lower the costs of deployment. In transportation engineering, there are many standardizing documents that take decision-making out of the hands of individual engineers, and into the handbooks. They also help alleviate legal challenges and personal liability risks to engineers and agencies that employ them. It remains to be seen whether the revolutions in surface transportation (especially automation, electrification, and sharing) change the use of standards. The operational characteristics of AVs when deployed at scale will require updating capacity numbers in the Highway Capacity Manual and the reaction times in the Highway Safety Manual. However, the risk remains that without steady adaptation of standards, roads will be mis-designed for the last technology long after it has been outdated. Standards that design road lanes wide enough for the variability of human drivers will be wider than needed for AVs and increase the total amount of pavement laid. Connected and Collaborative Institutions This section explores how the institutions themselves may be impacted by the same technology drivers affecting the transportation sectors, particularly how connectivity among users and institutions, as well as among agencies might impact their performance and that of the transportation systems they oversee. The direct and indirect opportunities introduced by the transformative technologies on the operation and performance of transportation organizations in the delivery of mobility services are examined. Collaborative Decision-making Structures Many of today’s transportation facilities and travel corridors extend across multiple jurisdictions involving several agencies that may or may not share the administrative duties of traffic management and control on the infrastructure. While the agencies involved share a common goal of efficient management of traffic and mobility systems, inconsistencies in policies and procedures and incomplete communication and decision processes may hinder the achievement of that goal. Problems arise when different jurisdictions have different procedures for dealing with events such as See https://www.allianceforparkingdatastandards.org/. 21 Transformative Technologies in Transportation121 incident management, police monitoring, or provision of emergency services. Decision makers have difficulty in obtaining consistent and reliable information on incidents or events that might occur near but outside the jurisdiction border. Urban planners and transportation officials have long recognized the importance of inter-jurisdiction coordination and collaboration in regional planning. Metropolitan planning organizations (MPOs) provide urbanized areas with an agency that facilitates land-use planning and transportation planning across jurisdictions. Through the ITS programs discussed in Chapter 2, coordination efforts of transportation agencies within several metropolitan areas resulted in the integration of services within a travel corridor, thus creating the concept of integrated corridor management. Jurisdictional boundaries are often areas of ambiguity from the standpoint of scope of authority; as a result, some services may not be sufficiently provided. These issues arise in the realm of police patrols and enforcement, response to incidents and emergencies, traffic signal control and coordination, transit and mobility service provision by multiple agencies (including private providers). In some life-threatening situations, such as firefighting, these issues have been addressed quite successfully and effectively, with inter-jurisdictional collaboration. However, such collaboration varies considerably in scope and effectiveness in traffic management, transit service supply, and mobility service delivery. In environments where multiple agents compete for the use of shared resources - from the roadway capacity, to landing and take-off slots at airports during peak periods and weather events, to sharing rail slots between freight and passenger service operators - flexible and responsive means for utilizing slots become essential to attaining the desired service levels and associated efficiencies. Such flexible means fall under the general umbrella of Collaborative Decision Making (CDM) schemes, which constitute a class of approaches for the management of shared or public resources by a collection of private and public entities or agents with individual goals. For problems in which competing agents such as airlines or private rail service operators have either an opportunity or a necessity to cooperate, an improved solution for each agent might be achieved through CDM. CDM has been applied in many areas, such as air traffic flow management, supply chain systems, submarine command and control, engineering design projects, and homeland security problems. The aviation industry, in particular, has taken significant interest in CDM models to help manage the increasing demands on airspace and air traffic, especially during capacity restrictions. For instance, under severe weather conditions, departure and arrival slots at airports are significantly reduced; CDM provides a framework for allocating slots to minimize delays incurred at congested airports, while ensuring equitable allocation to the competing airlines. Sharing of situational information among the involved stakeholders is essential to resolving problems in a quick, efficient manner. Equity is a major issue in developing the problem resolution. A key component to assure equity is resource allocation. In asset management, highway agencies have shown increasing interest in developing resource allocation methods to evaluate and decide when improvements or maintenance need to be made. CDM and Transformative Technologies in Transportation The transformative technologies that form the focus of this report interact with CDM among connected institutions in two inter-related respects. First, the transformative technologies themselves enable new transportation services and business models that call for greater coordination among institutions in both the public and private sectors, both to achieve the potential Transformative Technologies in Transportation122 of these technologies in serving communities in an equitable and effective manner, as well as to ensure the safety and integrity of these services (data privacy, cybersecurity). Second, the transformative technologies enable CDM across both existing and new connected institutions and enterprises. Examples of the first aspect abound: • The provision of integrated MaaS in urban environments, extensively discussed in Chapter 3 • Intelligent management of multimodal systems that include AVs and micromobility tools, along the lines discussed in Chapters 2 and 3 • Freight marketplaces that match shippers with carriers and other freight mobility suppliers in real-time, discussed in Chapter 4 CDM involves teamwork through communication, cooperation, and coordination among each agent in the team. While earlier forms of CDM were envisioned and performed through debate and negotiation among a group of people, modern schemes rely extensively on sophisticated collaboration support systems that allow most activities and interaction to occur virtually through well-defined frameworks and protocols. The promise of connected transportation systems enables all parties in a CDM to receive information about the state of the system, including assets, travelers, and commodities in real time. Similarly, a shared collaboration platform enables the selection and implementation of response measures across multiple agents – both public and private. Automation further enables responsive interventions through both software and hardware, augmenting human capabilities. As discussed in Chapter 2, AI techniques can play a significant role in sorting through massive real-time data streams to monitor, predict, and support interventions as appropriate. For an inter-jurisdictional or multi-agency CDM process to be successful, there first needs to be communication among jurisdictions and the corresponding agencies and stakeholders involved. Specifically, there needs to be a willingness to communicate information regarding the scenario. An agreement on the level of information sharing needs to be reached by all parties and can be either informal or formal. As an example, in the realm of intelligent roadway management, each jurisdiction could be responsible for maintaining its own information database and extract pertinent information, should an incident require collaboration and information sharing with other jurisdictions. Another option is to maintain a quasi-dynamic network of information databases, where each jurisdiction is still responsible for maintaining its own database but also distributes the latest copy to other jurisdictions in a communication sharing agreement. However, there are concerns about the possibility of duplicating information or occurrence of discrepancies that may hinder the cooperation and collaboration effort. Further, agencies and their constituents may not as comfortable about disseminating information freely to other jurisdictions. This issue is exacerbated when private sector entities are involved, and the information itself becomes part of the companies’ competitive advantage, or when cybersecurity or data privacy risks are present. Regardless of the information sensitivity or the willingness to share or distribute databases, a consistent agreement of communication and information sharing must be reached by all jurisdictions affected by potential inter-jurisdictional decision-making conflicts (Logi and Ritchie, 2002). Critical to the above process is sharing information about the events that trigger the CDM process. The technological capabilities discussed in this report enable far greater sensing and monitoring than previously available. Information from different sources maybe available to different entities. Processes for reconciling conflicting detection information across entities need to be part of a CDM process; the expediency of such information conflict resolution is considerably facilitated by the deployment of shared dashboard augmented with AI algorithms. Transformative Technologies in Transportation123 Automation further enables acting agents participating in a CDM platform to intervene, preventing delay and mitigating disruptions to the system. Through AI-based algorithms and software, the key functions could be automated, enhancing human capabilities in CDM environments. Besides, hardware could deliver services when human abilities are limited, for example, AVs could be dispatched to hazardous locations, drones can be dispatched to inaccessible areas, as well as for the emergency transportation of individuals. In summary, the second section described the concept of connected institutions, by which the transformative technologies under consideration could effectively enable enhanced capabilities in managing these technology-heavy systems. Specifically, it highlighted the possible opportunities to enhance information sharing and human interaction in these emerging contexts, using CDM structures augmented by real-time data streams and AI algorithms. Policy Implications Policy support plays a vital role in driving the adoption of transformative technologies in the transportation sector. By establishing enabling regulatory frameworks, fostering innovation-friendly environments, and providing targeted financial incentives, governments can create the necessary conditions for technology adoption to thrive. With effective policy support, the transportation sector can embrace and leverage these technologies to revolutionize mobility, create sustainable transportation systems, and propel economic and societal development. Embracing Open Data for Public Benefit Data is a source of power, and sharing information is not the prevailing practice unless required to enable minimal capabilities. Consumer benefits resulting from standards such as GTFS, which enable online transit routing, are fairly clear. In cases where a public agency is the data provider, it is perhaps easy to see how benefiting users also benefits the general public. Yet, there are limitations on public agencies to release data, often due to concerns over data privacy, resource constraints, proprietary standards, or a lack of incentive. On the other side, private sectors are understandably reluctant to reveal market-sensitive information about customer locations and travel patterns. When a private organization is the data provider, commercial considerations may create a conflict between private and public good. This can be overcome by rules and regulations established by funding organizations requiring participation in open standards and open data processes. When the data is standardized, applications can be built that ingest it, process it, and provide useful outputs. There is no need to reinvent the data filter or processing logic for every distinct organization. These standards, while they have a fixed cost to create, offer benefits for all that follow it. Hence, it is crucial to foster a culture of openness and establish regulations that mandate participation in open standards and data processes, especially for funding organizations. Driving Innovation through Standardization and Leveraging Platform Power Governments have a range of policy instruments, including platform powers, which enable them to drive innovation through research and development and shape interactions through standards. Information standards, in particular, play a critical role in fostering cross-organizational awareness and facilitating integrative applications. The maturity of standards greatly influences their adoption rate, making them a valuable asset for countries with less developed transportation systems. Transformative Technologies in Transportation124 By adopting existing standards, countries can both reduce the costs associated with innovation, as well as expedite the deployment of transformative technologies. Standards are valuable for exchanging information between institutions and enable new institutions to emerge on the information ecosystem. Successful standards can be thought of as platforms serving two-sided markets. There needs to be advantages to the potential users of the standardized form of data and the data providers releasing the data in a standard form. This happens more frequently in markets with many players, as a dominant player may not feel the need for an open standard. Standardization and innovation are ever in tension. Rules tend to harden, constraining the set of operational practices, and removing degrees of freedom for planners and engineers. As such, standards may reduce the opportunities for innovation. Generally, there should be open standards for information interchange to benefit users, providers, and regulators. The specifics of the standards need to be negotiated among the parties and emerge through a consensus process. Governments can help this with proactive adoption of standards, incurring the initial costs that standards may impose to achieve longer-term benefit. Learning From Success and Failure to Accelerate Future Innovations As the pursuit of enhanced sustainable mobility and equitable access continues, many questions remain unanswered and ample opportunities exists for innovations in crafting solutions to the complex issues likely to emerge along the long road to adoption and deployment. The aim is to offer enhanced levels of sustainable mobility and user convenience, while ensuring equitable access to mobility for all communities. There are numerous ways to accelerate the development of essential mobility services, such as by using digital money for payments, sharing services, electrification of fleet before the wide adoption of the ICE vehicles, or the use of aerial drones for medicine delivery rather than waiting for highways to be built and trucks to run. The adoption of digital payment systems can enhance convenience and efficiency in transportation transactions and developing countries can overcome traditional barriers associated with cash-based systems, promote financial inclusion, and facilitate seamless transactions for mobility services. Another observation involves leveraging sharing services to optimize resource utilization and reduce negative environmental impacts. Sharing economy models, such as car-sharing and ridesharing, enable the efficient use of vehicles and reduce the need for individual car ownership. By encouraging the adoption of sharing services, developing countries can alleviate the financial burden associated with private vehicle ownership while improving access to transportation for all segments of society. Furthermore, prioritizing the electrification of fleets before the widespread adoption of ICE vehicles can bring substantial environmental benefits. By investing in EVs and supporting the development of charging infrastructure, developing countries can mitigate air pollution, reduce GHG emissions, and transition towards a cleaner and more sustainable transportation system. Innovative approaches such as utilizing aerial drones for medicine delivery can also contribute to equitable access to essential services. Rather than waiting for extensive highway networks and traditional delivery methods to be established, deploying drones for medical supply transportation can overcome geographical barriers and provide timely healthcare access to remote and underserved areas. To accelerate technology adoption and deployment, it is crucial to learn from documented lessons and best practices. Analyzing successful case studies and understanding failures can provide valuable insights and guidance for developing countries as they navigate through the complex and rapidly evolving transportation landscape in terms of technology adoption. 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Annexures Transformative Technologies in Transportation129 Annex 1. Existing and Emerging Shared Modes Concepts and Examples Fleet Sharing Bikesharing: A service that provides travelers on-demand, short-term access to a shared fleet of bicycles, usually for a fee. Bikesharing service providers may own, maintain, and provide charging (if applicable) for the bicycle fleet. Motorcycle and Moped Sharing: A service that provides the traveler on-demand, short-term access to a shared fleet of motorcycles and/or mopeds for a fee. Service providers typically own and maintain the vehicle fleet and provide insurance, gasoline/charging, and parking. Scooter Sharing: A service that provides the traveler on-demand, short-term access to a shared fleet of scooters for a fee. Scooter sharing service providers typically own, maintain, and provide fuel/charging (if applicable) for the scooter fleet. Service providers also may provide insurance. Scooter sharing is sometimes referred to as shared micromobility. Ride and Delivery Services Auto Rickshaw: A motorized version of the pulled rickshaw, or cycle rickshaw that is sometimes used as a taxi. Typically, auto rickshaws have three wheels and an open frame. Sometimes referred to as “baby taxis” in Bangladesh; “bajaj” in Tanzania and Ethiopia; “toktok” in Egypt; “tuk-tuk” in Cambodia, Madagascar, South Africa, Sri Lanka, and Uganda; “keke-marwa” in Nigeria; “raksha” in Sudan; and “kekeh” in Liberia. Courier Network Services (CNS): A commercial for-hire delivery service for monetary compensation using an online application or platform (such as a website or smartphone app) to connect freight (for example, packages, food) with couriers using their personal, rented, or leased vehicles, bicycles, or scooters. e-Hail: Smartphone apps that supplement street hails by allowing on-demand hailing of taxis. It can also provide travelers with pre-arranged and/or on-demand access to a ride for a fee using a digitally enabled application or platform (such as a smartphone apps) to connect travelers with drivers using their personal, rented, or leased motor vehicles. This is commonly referred to as “transportation network companies (TNCs)” and “ridehailing” and “ridesourcing” in the U.S. and Commonwealth countries and “Voiture de Transport avec Chauffeur (or VTCs)” in Francophonie countries. Jitney: Typically, an informal, unlicensed, or illegal for-hire private transportation or taxicab operation. Microtransit: A technology-enabled transit service that typically uses shuttles or vans to provide pooled on-demand transportation with dynamic routing. Pedicab: A for-hire ride service in which a cyclist transports traveler(s) on a tricycle with a passenger compartment. Pooling: The formal or informal sharing of rides between drivers and travelers with similar origin-destination pairings using mopeds, motorcycles, or motor vehicles. Riders may share some trip costs (such as fuel). Shared Automated Vehicle (SAV): A service allowing automated vehicles to be shared among multiple users. SAVs can be summoned on-demand or operate a fixed-route service. Transformative Technologies in Transportation130 Taxis: A service that provides the traveler with pre-arranged and/or on-demand access to a ride service in a motor vehicle or motorcycle for a fee. The latter is sometimes referred to as a motorcycle taxi. The travelers typically can access this ride by scheduling trips in advance by street hail or a smartphone app. TNCs (also known as ridehailing, ridesourcing, and VTCs: A service that provides a traveler with pre-arranged and/or on-demand access to a ride for a fee typically using a digitally enabled application or platform (such as smartphone apps) to connect travelers with drivers who use their personal, rented, or leased motor vehicles. Digitally enabled applications are typically used for booking, electronic payment, and ratings. This is known as a dual-sided market, as it represents both the supply and demand side of a ride service operating between privately owned vehicles/ drivers and passengers via an app platform. Aerial Services Advanced Air Mobility (AAM): A broad concept focusing on emerging aviation markets and use cases for passenger mobility, goods delivery, and emergency services for urban, suburban, and rural operations. AAM includes local use cases of about a 50-mile (80 km) radius in rural or urban areas and intraregional use cases up to a few hundred miles. Uncrewed Aircraft Systems (UAS): This is an uncrewed aircraft, which includes associated support equipment, control station, data links, telemetry, communications, and navigation equipment needed to operate. UAS can be operated autonomously or remotely piloted. Mobility Integration and Aggregators Mobility-as-a-Service (MaaS): An integrated mobility marketplace where travelers can access multiple transportation services over a single digital interface. Brokering travel with suppliers, repackaging, and reselling it as a bundled package is a distinguishing characteristic of MaaS. The primary emphasis of MaaS is passenger mobility (allowing travelers to seamlessly plan, book, and pay for a multimodal trip on a pay-as-you-go and/or subscription basis). Mobility on Demand (MOD): MOD offers users access to mobility, goods, and services on demand by dispatching or using informal shared transportation services (such as auto rickshaws and jitneys), shared mobility, delivery services, and public transportation strategies through an integrated and connected multi-modal network. It is based on the principle that transportation is a commodity where modes have economic values that are distinguishable in terms of cost, journey time, wait time, number of connections, convenience, and other attributes. MOD emphasizes the commodification of passenger mobility, goods delivery, and transportation systems management. Key similarities between MOD and MaaS are their emphasis on physical, fare, and digital multimodal integration. Super App: Allows users to access several mobility, payment, retail, communications, and other services from a single digital interface. Transformative Technologies in Transportation131 Table A.1. Examples of Fleet Sharing Service Providers in Developing Countries Carsharing and Motorcycle Sharing Shared Micromobility Africa Locomute Medina Bike, Mobike Eastern Europe BelkaCar, Traficar bikenow, I’velo ? South Asia/India Drivezy, Hayr, Myles, Ola Drive, Revv, Bounce, SmartBike, Vogo, Yulu Volercars, Zoomcar Latin America Awto Grow Middle East AutoTel, Ekar, Mobilizm, Mobicar, Careem, KIWIride, Lime YOYO, Zipcar Southeast Asia Drivemate, GoCar, Hipcar, Haupcar, Anywheel, LinkBike, Pun Bike Share Moovby, Roadaz Source: World Bank. Table A.2. Examples of Ride Services in Developing Countries Demand- Informal e-Hail (Passenger e-Hail (Food and Responsive and Shared Ride Mobility) Goods Delivery) Informal Transit Services Africa Coursa, Gokidok, Bolt, Gozem Careem, Matatus GoVoiturage, Gozem, Little, (multiple Partagi, Jumpin taptap, Taxify, providers), Uber, Rides, Jekalo Teliman, Uber, Swvl Roby, ZayRide Eastern Europe MyTaxi DoorDash BlaBlacar (formerly Wolt), Bolt and Yandex Taxi South Asia/India Bikxie, Ola, Uber Bikxie Swvl BlaBlaCar, Poolmycar, Quick Ride, SRide Latin America Beat, Cabify iFood, Mercadoni Jetty, Shotl BlaBlacar Middle East Careem, Fyonka, Careem Careem, Uber, Darb Pink Taxi, Uber Swvl Southeast Asia Go-Jek, Grab Gojek, Grab Pun Pun Bike Share Source: World Bank. Transformative Technologies in Transportation132 Annex 2. Policy Evaluation Framework for Passenger Mobility Access A mobility policy evaluation framework for transportation access barriers would help the public sector to identify, understand, and overcome spatial, temporal, economic, equity, and social barriers. The framework presented in table below is adapted from the original STEP framework proposed by Shaheen et al, 2017. Spatial factors include challenges such as lack of service availability in a particular neighborhood, excessively long distances between destinations, and the lack of public transit within walking distance. Temporal barriers include time factors that can inhibit a user from completing time-sensitive trips (such as arriving at work) or making trips late at night when there are limited or no public transit options. Economic challenges include direct costs, such as fares, tolls, and vehicle ownership costs, as well as indirect costs (such as smartphone, Internet, credit card access) that create economic hardships or preclude users from completing basic travel. Physical considerations include physical and cognitive limitations that make using standard transportation modes difficult or impossible for certain individuals, such as children, older adults, and persons with disabilities. Social factors include social, cultural, safety, and language barriers that create challenges for travelers. Examples of social barriers can include neighborhood crime, poorly targeted marketing, and the lack of multi-language information. Table B.1. Policy Evaluation Framework for Passenger Mobility Access Barriers Definition Opportunities Challenges Potential Policies Spatial Spatial factors First- and last-mile Lack of satellite, Enhancing data that compromise partnerships data, and/or services in rural/ daily travel needs mobile coverage in less urbanized (excessively long Services designed remote areas that communities distances between for diverse built could inhibit access destinations, lack of environments (urban, to technology- Improving public transit within suburban, rural) enabled services infrastructure for walking distance) active transportation, particularly in urban and less urbanized areas Partnerships with mobility service providers for gap filling services in lower density/less urbanized built environments Temporal Travel time barriers Dynamic on-demand Wait time and Congestion pricing that hinder a user services travel time to manage supply from completing volatility on and demand of time-sensitive trips, Late-night and off- congested the transportation such as arriving to peak services roadways network. work (public transit Unpredictable reliability issues, wait times due to Partnerships with limited operating supply fluctuations mobility service hours, traffic providers for late- congestion) night and off-peak transportation Transformative Technologies in Transportation133 Definition Opportunities Challenges Potential Policies Economic Direct costs (fares, Subsidies for low- Formalization of Low-income tolls, vehicle income users informal labor/ pricing and subsidy ownership costs) mobility practices programs to expand and indirect costs Multiple payment affordability. (smartphone, options (cash, credit/ Driver exploitation internet access, debit, mobile) from app-based Telephone, flag, and credit card access) services (long other manual/low-tech Public Wi-Fi and/ hours, low wages, dispatch options that create or digital kiosk limited benefits) economic hardship access for digitally Cash payment options or preclude users impoverished users Affordability from completing Partnerships that to enable mass basic travel provide mobility market access for workforce development and job access Physical Physical and Specialized services Providing adequate Subsidies and cognitive for older adults, training drivers partnerships for limitationsthat children, people with and accessible mobility service make using standard disabilities, and other equipment providers and/or transportation populations with (adaptive vehicles/ vendors that provide modes difficult or unique needs devices) for users specialized services impossible (older for older adults, people adults, people with with disabilities, disabilities) and other special populations Social Social, cultural, Targeted outreach Inclusion of Specialized services safety, and to diverse sub- marginalized for a variety of social language barriers populations groups and cultural needs that inhibit a user’s (transportation comfort with using Service information Safety and for women, ethnic/ transportation in user’s native security concerns religious minorities, (neighborhood crime, language/dialects for vulnerable and others) poorly targeted users (children, marketing, lack women, minorities) of multi-lingual support) Source: World Bank. Transformative Technologies in Transportation134 Annex 3. Methodological Framework for Evaluating the Impacts of CAV Technology While connected and autonomous vehicle (CAV) technologies have the potential to improve existing transportation systems, transportation agencies would like to be able to evaluate and identify which applications best address the unique transportation problems in their areas, considering expected impacts and resource requirements, without the need for full-fledged field experiments. Transportation analysis tools provide an efficient means to evaluate transportation improvement projects prior to implementation. Most existing methods typically used in the appraisal of proposed transportation projects and technologies have only limited ability to evaluate CAV technology applications due to their inability to incorporate vehicle connectivity as well as autonomy. Furthermore, guidance on how these traffic tools might be adapted and/or extended to evaluate CAV applications is only beginning to emerge in selected advanced economies, and typically for selected narrow applications. At the strategic level, transportation agencies use sketch planning tools and travel demand models to evaluate transportation improvement projects. Sketch planning tools rely on aggregated data and outcomes from past projects to produce estimates of potential improvements for future transportation projects. Current sketch planning tools are unable to estimate the impacts of CAV technology applications, which would need to be informed by literature and past studies. Similarly, travel demand models use current conditions, demographic forecasts, and employment trends to predict future benefits and impacts of transportation improvement projects. However, existing travel demand models are unable to account for mode shifts associated with CAV technology. The apparent lack of analysis tools for CAV technology applications prevents effective and accurate transportation planning processes, and consequently, the ability of agencies to deploy these technologies; planning is where deployment starts. Traffic simulation tools, which are often used to evaluate a wide range of operational strategies or roadway modifications across an entire network, lack the appropriate car-following and lane-changing behavior required to model and estimate the impacts of CAV technology applications. In addition, existing off-the-shelf traffic simulation tools are unable to mimic the communications and sensing capabilities present in CAVs. To quantify/evaluate the full benefits of CAV technologies on the transportation system, transportation agencies must be equipped with all the necessary traffic analysis tools needed to predict potential impacts and support decision-making, both at the planning and operational levels. Major Components of the Methodological Framework for Strategic Impact Evaluation To correctly evaluate the impacts of CAV systems, they should be analyzed on the strategic level as well as the operational level. An integrated methodological framework is presented below (Figure C.1) to assess the changes entailed by CAV technology to the supply and demand on the strategic level, and to flow performance on the operational level. This section will describe the different components of the framework and their integrations within the whole system. Supply Changes While there are no models available to predict the emergence of new mobility options, expert judgement and market trends can be utilized to specify operational scenarios for emerging options and plan for them accordingly. For instance, the well-known interest of TNCs, such as Uber and Transformative Technologies in Transportation135 Lyft, in AVs makes shared-automated-vehicle (SAV fleets, a new mobility mode, a high possibility that should be taken into consideration in future mobility plans. Although the exact behavior of emerging modes is not entirely clear and is likely to be different from current systems with human drivers, planners can create different scenarios for the operation of emerging mobility options, such as automated shared systems, and assess their impacts on travel behavior like mode choice and network performance. In addition to introducing new mobility options through AVs, connectivity can change the way real-time information is extracted and used for active management systems through V2I/V2V telecommunications. For instance, more information about prevailing traffic conditions can be generated from CVs than traditional data generation methods such as radars and loop detectors, potentially improving active management algorithms. Integration of the Supply Changes within the Methodological Framework The objective of the supply changes component in the general framework is to define different scenarios for the operation of emerging mobility options and new technologies. As it is difficult to design tools that can predict the emergence of new supply options, those scenarios will be determined based on market trends and experts’ judgment. The assumptions about supply changes are integral to evaluating demand changes and operational performance. As illustrated in the framework, the defined scenarios will determine the availability of new modes, such as SAV fleets, and the type of new telecommunication technology in place, whether it is V2I, V2V, or V2X communications. Those assumptions will define the system configuration, regardless of its scale (network, corridor, or a facility), based on which demand changes and operational performance will be evaluated. The characteristics of the new mobility options are essential for assessing the extent to which the new options will influence travelers’ behavior and mode choice. For example, the cost and reliability of the new service will affect its competitiveness against other options, and consequently, the prediction of its market share. For that reason, assumptions/scenarios have to be regularly updated as more information becomes available about the new service or technology. In addition, demand and performance models have to be robust to integrate the updated characteristics as they become available. Demand Changes The new forms of mobility and enhancements that CAV technologies bring to current transportation systems could affect demand patterns on different levels. On the higher level, vehicle ownership in households will be affected as AV technology can make sharing vehicles more efficient and convenient. This can lower the number of vehicles needed by households or eliminate the need for it altogether, if the new service is proved to be reliable and financially competitive. On the medium level, activity patterns of households can be affected by the new technology. Allowing multitasking while being driven in AV may change the value of in-vehicle time. People may travel longer distances as they would be able to do some tasks while driving, like working or watching a movie. Furthermore, having a robotic “chauffeur” to assist in daily chores can reprioritize activities in the household. For instance, highly autonomous vehicles could pick up kids from school or groceries from the store. Transformative Technologies in Transportation136 On the lower level, travel routes, mode choice, and departure times can be affected by CAV systems. Connectivity can impact route choice by sharing traffic conditions between vehicles or between vehicles and the infrastructure, leading to better estimates of travel times and the shortest paths. In addition, AVs can dynamically reroute themselves as they receive more information about the network traffic conditions. In addition to route choice, new mobility options will affect mode choice by travelers. As connectivity would allow better integration between modes, travelers may choose to use multiple modes simultaneously, like using ride-sourcing and transit, or entirely shift to different modes. Figure C.1. Methodological Framework for Evaluating the Strategic Impacts of CAV Technology Demand changes Supply changes Activity systems and New mobility mobility choices industry options Demand Technology patterns Demand models (Activity and travel behavior) Operational performance models (Flow simulation) Activity choices Engagement, Duration, Sequencing and chaining with whom, etc... Transportation system attributes Travel choices Performance measures Destination, Mode, Trip timing Travel time path choice Reliability Availability Comfort Safety Source: World Bank. Transformative Technologies in Transportation137 Integration of Demand Changes within the Methodological Framework The objective of the “demand changes” component in the general framework is to predict activity patterns and travel choices, influenced by CAV technology and new mobility options as defined in the scenarios produced from the “supply changes” component. The demand changes, such as trip duration; mode choice; and timing, will be predicted using robust demand models that are also integrated with performance models. Demand changes such as longer trip durations, higher number of trips, or the paths chosen; influence the demand flows used in performance models to evaluate the new system’s attributes in the presence of CAV technology at the operational level. The new attributes, such as travel time, comfort, and reliability produced by performance models, for example, dynamic traffic assignment tools or microsimulation, will update mode characteristics in demand models and reproduce demand flows. The loop of updating demand flows and system attributes exchanged between demand and performance models stops when a convergence criterion, or multiple, is met. Examples of such criteria include activity schedule consistency and improvement in travel time. Operational Impacts of CAV Technology One of the most immediate and direct impacts of CAV technology on current transportation facilities is enhanced traffic flow. The technology offers the potential for increased throughput and more stable flows compared to regular vehicles at high market penetration rates. However, it is likely that the market penetration of those CAVs will be low in the early period of its introduction, leading to mixed-traffic behaviors on the road. First, there will be isolated manual drivers who have relatively higher reaction times and risks of driving errors. Second, there will be connected and well-informed drivers who are more aware of their surroundings and presumably, with better reactive behavior. Finally, there will be the new robotic driving behavior with the introduction of highly autonomous vehicles, which can also be connected through wireless telecommunications. This behavior would heavily depend on the equipped sensors and the control algorithms installed by car manufacturers, besides the additional information that can be received through connectivity. Integration of Operational Impacts within the Methodological Framework Evaluating the operational performance impacts of CAV technology is the third component of the strategic framework, shown in Figure C.2. The objective of this component is to evaluate transportation systems’ performance under the operational scenarios and demand patterns predicted/assumed at the strategic level. The performance simulation tool is envisioned as an integrated traffic-telecommunication simulation platform that can simulate mixed traffic conditions under different operational assumptions and scenarios. The conceptual framework of the tool, shown in Figure C.2, includes four types of driving behaviors: (1) isolated-manual, (2) connected-manual, (3) isolated-automated, and (4) connected-automated. It also includes a wireless telecommunication component, which specifies the type of telecommunication network and its operation. Finally, the tool includes a component to simulate the heterogeneous interactions among the different driving behaviors, depending on the assumed connectivity/automation levels and the implemented control algorithms. Transformative Technologies in Transportation138 Image A.1. AI tracking traffic vehicle car recognizing sensor Source: Adobe Stock. As inputs to the integrated simulation platform, the framework of the performance simulation tool includes traffic volumes and the system configuration, which are influenced by CAV technology at the network-level. In addition, external factors (such as weather), OEMs’ logic for controlling AVs, and the agency’s communication protocols are considered in the integrated simulation platform. Finally, the simulation tool outputs pre-defined performance measures to evaluate the impacts of CAV technology on traffic flow. Performance Measures Table C.1 summarizes the key categories of performance measures in evaluating the impact of CAV technologies on system operational performance. Transformative Technologies in Transportation139 Figure C.2. Conceptual Framework of the Performance Simulation Component Demand patterns System configuration Traffic volumes Facility type avCAV market penetration geometry technology Intergrated traffic-telecom simulation platform Wireless telecommunications V2V, V2I Technology sensor performance/range reliablity Connected manual Connected Isolated manual driver behavior Isolated automated automated driver driver behavior Connectivity levels driver behavior behavior Acceleration Acceleration Automation levels Automation levels choice/car following choice/car following acceleration acceleration lane changing lane changing choice/car following choice/car following multi-lane interations response to control lane changing lane changing actions Heterogeneous traffic interactions Different automation/connectivity levels special control algorithms using data generated by CAV advanced algorithms for traffic signal control Performance measures OEMs Safety Updated robotic Throghput logic for self- Flow stability driving cars Sustainability (if applicable) External factors Agency Weather incidents Communication special events protocols work zones (rules of high demand engagement) Source: World Bank. Transformative Technologies in Transportation140 Table C.1. Summary of CAV Performance Measures Category Impact Performance Measure Safety Improve safety outcome Surrogate safety assessment Traffic flow volumes Number of vehicles per hour per lane Variability of speeds within traffic Smoothness of traffic flow stream Throughput Corridor/Intersection capacity Green occupancy ratio utilization Intersection degree of saturation Intersection control performance Control delay Local stability Local flow stability index Flow stability String stability Mixed-flow string stability index Number of significant shockwaves Occurrence of traffic shockwaves formed Flow break- down and Propagation speed of formed reliability Severity of shockwaves shockwaves relative to wave front Duration of shockwave-induced queues Impact on GHG emissions Level of equivalent emissions Sustainability Energy consumption Amount of energy consumed Source: World Bank. General Assumptions While CAV systems have been studied in the literature, theoretically and experimentally, their actual behavior on the road is still not entirely clear to researchers. The actual behavior will depend on the technology and algorithms that will used in those systems at the time of deployment and is likely to be different from the tests conducted in a closed environment. Furthermore, predicting how humans will react to the new technology is even harder than predicting how technology will operate, knowing that human behavior is more complex to model than technology. Therefore, the AMS system for evaluating CAV technology impacts will have to utilize some assumptions regarding the behavior and operations of the new systems, and should be robust to update those assumptions when more data is available. For predicting changes to the supply of new mobility options, some assumptions will be made regarding the technology deployed in the new systems, their integration with traditional systems (SAV and Transit), and their area-availability (urban, suburban). This will generate multiple scenarios that can be evaluated and updated. For predicting demand and behavioral changes in response to Transformative Technologies in Transportation141 the new technology, surveys can be a good starting tool to gain insights and build demand models, but then the general assumption would be that this behavior would not change once the technology is operational, which may not be entirely true. For modeling the operational performance of the new system, some assumptions will entail different parameters for modeling the car following and lane changing behavior of CAV systems, such as reaction time and gap acceptance. While new data sets are emerging from experiments on the systems, the actual behavior can be different. Other assumptions will involve the flow of information between the vehicles themselves and the infrastructure, which will depend on the wireless telecommunication technology that will be used. Finally, some assumptions will involve the interactions between CAVs, pedestrians, and bicyclists. Limitations The limited availability of actual data on the behavior and operation of CAV systems and their interactions with travelers is the main limitation of the proposed autonomous mobility services (AMS) system. This limitation applies to the three main components of the system: 1) supply changes, 2) demand changes, and 3) operational performance. For predicting the emergence of new mobility choices, limited information is available on how new mobility options, such as SAV fleets, will operate as they become available. For example, will the new service be profit-oriented and centralized in highly populated areas of a city, which is a more lucrative option for TNCs, or would it be flexible enough to serve less-dense areas? The actual data limitation also applies to predicting the behavioral changes caused by CAV systems. While most research rely on stated-preference surveys, asking travelers what they would do if they had a CAV option, or driving simulations, the actual behavior is likely to be different. For example, some travelers may not trust CAV technology now, but may do so once the technology is widespread and used by their peers. Another example would be the case of demand models that rely on travelers’ stated choices for modes and routes. Using travelers’ stated-choices for building these types of models to predict the use of CAV systems may not be accurate as actual choices are likely to be different once the new technology is available. A similar story goes for evaluating the operational performance of CAV applications. Most of those applications, especially for AVs, rely on assumptions regarding how the new technology will operate once it is commercially available. For example, how close AVs will follow leading vehicles or what is the safe gap that is programmed in those vehicles to change lanes. Another example is how connected travelers would react to the new information available through wireless communications. Again, those assumptions may not capture the actual behavior or operation of the new systems, and the resulting performance measures may be misleading. On the other hand, these assumptions are essential for the AMS system to evaluate the new technology. Therefore, a scenario-based analysis is a good way to evaluate the impacts of the CAV systems. In addition, the AMS system should be robust enough to allow changing these assumptions as actual data becomes available. Transformative Technologies in Transportation142 Annex 4. Application and Impact of Artificial Intelligence in Transportation In view of the new opportunities and challenges AI may bring to the transportation sector in developing countries, this annex aims to distill lessons from current practices in both developing and developed countries and discuss how the use of AI in transportation could help developing countries leapfrog towards the next generation of intelligent and smart transportation. AI is a broad concept that computers and machines can emulate human capabilities or skills of problem-solving and decision-making. Human capabilities or skills are augmented by the process of learning from experience. In most cases, the development of AI is mimicking such a learning process, where computer programs are trained to learn from acquired/given information by adjusting their underlying algorithms to improve their capability. In recent years, AI has been adopted in many aspects in developing countries including improving the economy, protecting the environment, and benefiting social welfare. Those applications have shown the potential of AI in dealing with complex problems and performing high-risk activities. Many AI-based transportation systems apply data analytics and logic-based techniques, using machine learning (ML) as the engine, to collect data, interpret events, and make decisions. As data-driven methods, ML techniques learn and extract knowledge from transportation data. The transportation sector is a natural ground for leveraging existing infrastructures and applying AI technologies. Transportation, as a complex system, involves a large variety of tasks such as safety improvement, logistics management, travel demand forecasting, infrastructure preservation, traffic operations, multi-modal transportation coordination, and so on. The operation of transportation systems often requires great efforts in collecting, processing, and analyzing big data, for which human capital development is a prerequisite. However, in many developing countries, lack of skillsets and insufficient numbers of transportation professionals have caused the scarcity of transportation-related data, and thus, has inevitably created a barrier to provide efficient, safe, and sustainable transportation services. The capability of AI applications can offer new opportunities to overcome such burden because: • AI can help to acquire richer transportation data with less human effort, such as pavement monitoring based on computer vision. • AI can adapt itself for better performance in response to change of environment. Accordingly, AI presents a possible avenue for developing countries to leapfrog to meet future smart mobility needs by providing opportunities to leverage existing infrastructures through better utilization of their capacity. For instance, it can leverage existing roadside cameras to collect real-time traffic information at relatively low marginal cost. Such data can be further used by an AI algorithm (for example, deep learning) to design a more efficient traffic management plan. By analyzing road images captured by onboard vehicle cameras, AI can facilitate the process of detecting and rating risky conditions on roadways, based on attributes such as side slopes, shoulder width, striping, pavement condition, and guardrail usage. With limited resources, the use of AI technologies to optimize the performance of existing assets is a critical policy lever for developing countries to explore. Along with these new opportunities, there are key challenges in deploying AI-based transportation applications in developing countries. To ensure successful adoption of AI, particular attention needs to be directed to (Ajadi, 2020): Transformative Technologies in Transportation143 • Data privacy and security • Human capital to engage AI • Bias and inequality • Governance and accountability The use of AI could bring opportunities to advance the AI industry in developing countries, making it important to articulate policies that define the accountability structures and mitigate related risks. AI may also have a significant impact on the economy and reshape the transportation labor market in developing countries and widen economic disparities, as it offers a higher level of automation in the workforce. Such impacts could be minimized through government interventions. Moreover, AI-based systems often need regular maintenance, which would create new job opportunities for people with the appropriate skillsets. Therefore, to prepare the developing world for the changes brought by AI, it is important to: • Promote active labor market policies, specifically targeting those at risk of being left out, and increase the labor participation rate for the underrepresented groups. • Offer education, training, reskilling, and other learning programs on workforce development for AI skills. • Integrate AI with traditional transportation systems and best leverage existing transportation infrastructures. AI Development and Deployment for Transportation Generally, AI can be viewed as intelligent machines or computer programs that mimic humans or other intelligence to carry out certain tasks. Herein, intelligence is defined as the ability to acquire and apply new knowledge. An essential step in the development of AI applications is training the algorithm to interpret data from the environment. The goal of the training process is to use empirical data to teach AI how to take actions or make decisions under various circumstances. The empirical data should contain both input (for example, data from the environment) and the desired output (for example, correct actions). The intent of the training is to make AI learn a mathematical function that best approximates the relationship between input and output. When such data are available, several steps are typically followed to develop the AI algorithm. In the first step, the AI developer has to choose a proper algorithm architecture among many candidates based on the objectives and constraints of the application. In the second step, the developer needs to customize the algorithm architecture and use the obtained data to train the algorithm. Finally, it is often required to test the developed AI under different scenarios for performance evaluation. The current deployment of AI-based transportation applications is, so far, limited in developing countries. Didi Chuxing, a Chinese private company that provides mobile app-based transportation services platform, including taxi hailing, private car hailing, social ride-share, and on-demand delivery services, has been using AI algorithms to predict traffic jams to build predictive dispatching models for its ride-share vehicles. Leveraging the huge amount of transportation data collected by the mobile app, Didi Chuxing claims that it can forecast traffic congestion 15 minutes in advance, with 85 percent accuracy (Zoo, 2019). Given the forecasted traffic condition, Didi vehicles will be dispatched to the high-service demand areas before the transportation network becomes congested, resulting in reduced waiting times for users. How to use AI to gather enriched transportation data, with existing infrastructures and assets, is a critical issue for developing countries to explore. Transformative Technologies in Transportation144 Approaches for AI Deployment Several pathways are possible for deploying AI applications for transportation. For developing countries, three possible approaches are generally available, as listed in Table D.1 in descending order of effort. The third approach provides a compromise whereby an AI application developed by a third- party serve as starting point for additional improvement using data collected locally. The benefit of this approach is lower cost than if developed “from scratch”, with good performance driven by local data. Table D.1. Three Approaches of AI Deployment Description Advantages Disadvantages Approach 1: Develop a new AI AI performance is usually good High cost; require AI knowledge application for algorithm development; need to collect training data Approach 2: Deploy an Low cost; require minimal AI AI performance might be poor application developed by a knowledge for operations; no third-party need to collect data Approach 3: Deploy application AI performance could be Require certain AI knowledge; developed by a third-party and improved; reduced cost need to collect new data; the AI improve AI performance with compared with Approach 1 has to be open-sourced, or the new data developer need to be engaged Source: World Bank. Impacts of AI Economic Impacts of AI Having illustrated the potential of AI to improve road transportation in a variety of developing country environments, there is still a debate on the desirability of introducing AI technologies to developing countries since AI could disrupt industries and affect economics. The primary concern is that AI has automated many tasks that are traditionally completed by humans and would lead to a decline in the outsourcing of manufacturing jobs. Therefore, some believe that AI will have a negative impact on developing economies’ export-led growth model. Meanwhile, others have argued that new opportunities brought by AI could compensate for the loss of human jobs. On one hand, in some industries such as transportation, human jobs that AI could potentially displace do not even exist in abundance in those developing countries (De-Arteaga et al., 2018; Kshetri, 2020; Kshetri, 2021). For example, previous sections have illustrated the possibility of using AI for collecting transportation user data. Although such a task could also be manually completed through labor-intensive activities, very few transportation agencies in developing countries are currently doing so. This is mainly because the value of data is not significant unless its quality and quantity are sufficient to address transportation problems. Also, limited capital resources in developing countries often direct the allocation of investments to tasks with higher priorities such as infrastructure development. On the other hand, AI can be viewed as an economic growth driver through its effects on the total factor productivity (TFP). TFP measures the usage efficiency of Transformative Technologies in Transportation145 production factors such as capital and labor. By performing some tasks faster than humans, AI enables the possibility to function as a new workforce at a higher scale and speed. Further discussion on the potential impact of deploying AI in transportation should be explored. First, the transportation sector is different from other manufacturing industries. Government investments in transportation usually do not receive direct large payback immediately. However, transportation plays a key role in stimulating economic growth. Better transportation services can lower the costs of moving people and goods and consequently, increase economic productivity. Hence, the use of AI can help foster the development of more efficient transportation and bring positive economic impacts. Second, transportation in developing countries often suffers from three challenges - shortage of data, lack of engineering skillsets, and high demands for infrastructure investment. AI-enabled intelligence could help remedy the skillset gap as it can learn fast from experience (data), build knowledge quickly, and even improve itself over time with the learning capabilities. AI could not only assist transportation engineers to make better decisions, but also assist newly trained personnel to take actions that are traditionally left to experienced professional engineers. Although transportation infrastructure plays an important role in faster economic growth and alleviation of poverty in developing countries, resource limitation often creates obstacles in making sufficient investments in infrastructure development. The use of AI to get the best performance out of existing transportation assets is a critical policy lever for developing countries to explore. Opportunities Brought by AI Using AI to address transportation problems would require AI knowledge to develop, deploy, or maintain AI-enabled systems. Human capital development is a prerequisite for developing countries to adopt AI for transportation. For example, the development and deployment of AI often require the project team to have knowledge in both transportation engineering and AI. Therefore, investments in human capital are drivers that help developing countries leapfrog towards future smart mobility. In skill training programs and workforce development, AI could be designed to support trainers to deliver better content. For example, the education work can be de-tasked into several parts (De-Arteaga et al., 2018; Kshetri, 2020; Kshetri, 2021). When offering educational content, trainers can focus more on building up students’ emotional intelligence. AI can assess the students’ progress in learning and support the education with additional contextual information. Moreover, AI is capable of delivering individual supports for students, making modern education modes (such as online teaching) more effective and providing a better learning experience to the future transportation engineers. In addition, AI can bring new opportunities to the industry. In developing countries, many local technology hubs are rapidly evolving, where AI provides a foundation to foster technology deployment. For example, Ethiopia has launched high-profile AI initiatives to develop technology parks and stimulate AI industry development. New job opportunities can be created. Many developing countries are also increasingly involved in the global AI industry. For example, AI development is accompanied by the high demand of data for algorithm training. According to a 2018 McKinsey report (Chui et al., 2018), data is the biggest barrier in the AI industry. The need for labeled data has created hundreds of thousands of jobs in developing countries such as India, the Philippines, and Kenya. According to the analyst firm, Cognilytica, the data labeling market was worth $150 million in 2018 and is expected to grow to $1 billion in 2023 (Murgia, 2019). A good example in transportation is related to the AV industry. With the motivation of removing drivers, AVs need to understand the driving environment by analyzing data obtained by onboard sensors. Hence, it is critical to provide sufficient data of road signs and infrastructures, vehicles, pedestrians, and other road users to train the AI algorithm. Transformative Technologies in Transportation146 It should be noted that AI does not replace the current labor jobs in developing countries. Instead, it could potentially create new labor-oriented job opportunities such as data-labeling works. The growth of AI deployment in developing countries is likely to have spillover effects of AI knowledge to the local market. AI-enabled transportation systems developed in another region may not be transferable to developing countries due to the different traffic composition, driver behavior, demand level of pedestrians, and so on. Therefore, those AI applications need to be customized or re-designed to meet specific local needs. This will lead to high demand for AI engineers in the local job market and consequently, foster human capital development. Challenges for AI Deployment Developing countries could face various barriers and challenges to using AI in transportation. First, many decision-making processes in transportation require the engagement of engineering judgment. The implementation of AI-based systems for addressing transportation problems requires sufficient public trust in such new technologies regarding its safety, effectiveness, and reliability. Even though humans tend to make more mistakes, the public seems to have less tolerance for the mistakes by AI. The AI deployment process needs to be managed conscientiously with appropriate risk management strategies. Second, the use of AI for transportation is often associated with noneconomic costs such as loss of privacy. For example, many AI applications in transportation involve the usage of videos, smartphone data, which could raise concerns about privacy and security. AI in some developing countries may be developed without sufficient consideration of data privacy, partly to encourage AI innovation. The latter may be viewed as having overall greater net benefit than enforcing stringent data-privacy laws. Existing ties of developing countries with investors, customers, partners, and suppliers from developed counties can create barriers to AI industry development and obstacles to international collaboration. Cost Benefits of Implementing AI Solutions The implementation of AI solutions in transportation can bring both direct and indirect benefits to society. For the direct benefits, it can reduce both the monetary and time cost for the operation and maintenance of existing transportation infrastructure. Another direct benefit is that the implementation involves the local AI industry, which will create new job opportunities and contribute to the economic growth. For the indirect benefits, AI will result in better performance of existing transportation assets, further stimulating economic growth. The costs associated with the implementation can be classified into two categories: economic costs and noneconomic costs. The economic costs include the costs of office space, computer hardware, developing/customizing/migrating AI software depending on the use cases, operation and maintenance including labor and energy, and so on. There are also non-economic costs such as the cost of privacy violations and environmental degradation. It should be noted that although the AI industry is generally considered a “green industry”, its environmental impacts would be largely affected by the energy policy of the implementing county. Summary As a booming transformative technology, AI brings many opportunities and challenges to the transportation sector in developing countries. AI has been applied in the industry from planning to daily operation. Examples include traffic counts estimation and vehicle trajectory reconstruction Transformative Technologies in Transportation147 by AI-based computer vision, computer-vision-based roadway safety assessment, traffic state forecasting by machine learning, traffic signal timing based on deep reinforcement learning, computer-vision-based infrastructure assessment and management, AI-based taxi dispatching, and so on. Given such broad applications, AI can help mitigate the issues developing countries face in providing better transportation services. The most critical one is the lack of skilled transportation professionals. Most AI-based transportation applications require much fewer transportation professionals and less transportation-related skillset. It can be argued that the development of AI applications needs relevant AI professionals, but the labor requirements can be greatly lessened by transferring AI applications with similar functionalities that have been developed. AI could also be utilized to support trainers to deliver better content in training programs and workforce development. Moreover, AI-based data collection techniques can help to ease the data shortage issue faced by developing countries. The usage of AI techniques can also potentially save or defer investment cost in new roadway infrastructure by better utilizing the existing infrastructure. Additionally, the use of AI in transportation brings positive economic impacts. First, it can increase economic productivity by lowering the general costs of moving people and goods through safer and more efficient transportation services. Second, the development of AI can offer new job opportunities and improve total factor productivity in developing countries. Third, AI is highly unlikely to have negative employment impacts on the transportation sector since such jobs do not exist in abundance in transportation industries of developing countries. There are certain barriers and challenges to the use of AI in transportation in developing countries. First, it requires sufficient trust from both transportation agencies and the general public. Therefore, its deployment process needs to be managed conscientiously, along with risk management strategies. Second, the use of AI in transportation may be associated with the loss of privacy. Certain policies and regulations are needed. Three major approaches were discussed for AI development: • Developing a new AI application • Deploying an AI developed by a third-party • Deploying an AI developed by a third-party and improving AI performance with new data The first two approaches are generally not preferred for developing countries due to the high cost or low performance respectively. The third approach provides a cost-effective way for developing countries, but the customization of AI applications is still needed. In conclusion, AI can help developing countries provide safer, more efficient, and more environment- friendly transportation services with less investment, and thus, positively impact the whole economy. Therefore, developing countries should take this opportunity benefit society, as certain policies, regulations, and public outreach are needed to ensure the healthy deployment of AI in transportation. Transformative Technologies in Transportation148 Annex 5. Technology Investment in Transportation Projects at the World Bank Table E.1. Technology Investment in Transportation Projects at the World Bank Technology Area World Bank Project Region/Country ITS/ICT/satellite monitoring, Rural Mobility and Connectivity Niger, Chad, Mali, Guinea road safety data collection Projects ITS/Smart traffic signal Smart Mobility Program for Brazil São Paulo ITS/ICT/Smart driver license Eastern Africa Regional Kenya system, fiber optic network Transport, Trade and Development Facilitation Project ITS/Monitoring systems, smart Wuhan Integrated Transport China parking, data center Development ITS/Smart transit system, Transport Systems Ethiopia vehicle information system Improvement Project ITS/Mobile data Using Mobile Data to Sierra Leone Understand Urban Mobility Patterns in Freetown, Sierra Leone ITS/Smart city Smart City and Traffic Ukraine Management for Kyiv ITS/Management information Emergency Lifeline Yemen, Republic of system Connectivity Project ITS/Traffic control system, Ulaanbaatar Sustainable Urban Mongolia smart parking, MaaS Transport Project ITS/Bus operational control Sao Paulo Aricanduva Bus Brazil center Rapid Transit Corridor ITS/Digitalization, automatic Innovation in Fare Collection Kenya, Mozambique, Nigeria, fare collection Systems for Public Transport in Rwanda, South Africa African Cities ITS/Fare collection system Greater Beirut Public Transport Lebanon Project ITS/Emergency response Assam Inland Water Transport India system Project ITS/Air navigation and Solomon Islands Roads and Solomon Islands communication systems Aviation Project ITS/Signal system Railway Improvement and Egypt Safety for Egypt Project ITS/ICT/toll road Regional Connectivity and Azerbaijan, Kazakhstan Development Projects ITS/MaaS Adapting Mobility-as-a-Service Global study for Developing Cities Transformative Technologies in Transportation149 Technology Area World Bank Project Region/Country ITS/ICT/Fiber optic installation, Western Economic Corridor Bangladesh smart highway and Regional Enhancement Program ICT systems/border crossing Transport Corridors and Afghanistan, Azerbaijan, control/data sharing Regional Connectivity Project Bangladesh, Iraq, Mali, Nepal, Tanzania ICT/ITS/Fiber optic Horn of Africa Initiatives Ethiopia, Djibouti, Somalia network, traffic information management ICT/Railway system Sustainable Croatian Railways Croatia, Serbia digitalization in Europe/Serbia Railway Sector Modernization ICT/Road asset management Vietnam Road Asset Vietnam Management Project Digitalization/Maritime Accelerating Digitalization: Global study Critical Actions to Strengthen the Resilience of the Maritime Supply Chain Digitalization/Road asset Rural Transport Improvement Bangladesh management Project Electric mobility Economics of Electric Vehicles Global study in Passenger Transportation Electric mobility Green Your Bus Ride: Clean Argentina, Brazil, Chile, Mexico, Buses in Latin America Uruguay Electric mobility Electrification of Public China Transport: Case Study of the Shenzhen Bus Group Electric mobility Electric buses Cote d'Ivoire, Cameroon, Senegal, China, Chile, Brazil, Egypt, India Electric mobility Electrification of two- and Mali, Burkina Faso, Bangladesh, three-wheelers India Electric mobility Emobility Policy Strategy Rwanda, Kenya, South Africa, Cambodia, Lao, Nigeria, Ghana, Indonesia, Vietnam, Philippines, Serbia, Ukraine, Georgia, Kazakhstan, Colombia, Uruguay, Brazil, Egypt, India, Bangladesh, Maldives, Bhutan, Chile Source: World Bank. 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