The Case for Cycling Infrastructure Investments CyclingMax: A Cost & Benefit Scoping Tool for Decision-makers Feng Guo1, Jianhe Du1, Kazuyuki Neki2, Lama Bou Mjahed2, Georges Bianco Darido2, Wei Wang2, Ana Waksberg Guerrini2 https://cyclingmax.worldbank.org 1 Progress Analytics LLC 2 World Bank Group The Case for Cycling Infrastructure Investments ii © 2025, The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. 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The Case for Cycling Infrastructure Investments iii Table of Contents List of Tables���������������������������������������������������������������������������������������������������������������������������������������������������iv List of Figures��������������������������������������������������������������������������������������������������������������������������������������������������iv Acknowledgments............................................................................................................................................v Chapter 1. Introduction.................................................................................................................................... 1 1.1. Tackling barriers to financing active mobility infrastructure��������������������������������������������������������� 3 1.2. Empowering policymakers to make informed decisions ����������������������������������������������������������������� 3 1.3. Creating a pathway to optimize cycling infrastructure investments ������������������������������������������ 4 Chapter 2. The Case for Cycling Infrastructure������������������������������������������������������������������������������������������5 2.1. Safety benefits of well-planned cycling facilities������������������������������������������������������������������������������ 7 2.2. Positive environmental impacts of increased cycling infrastructure������������������������������������������� 9 2.3. Improved health and mortality rates for cyclists���������������������������������������������������������������������������� 10 2.4. Reduced travel time �������������������������������������������������������������������������������������������������������������������������������� 11 2.5. Broader impacts in other categories��������������������������������������������������������������������������������������������������� 12 2.6. Key takeaways����������������������������������������������������������������������������������������������������������������������������������������� 13 Chapter 3. Getting Started with the CyclingMax Tool ����������������������������������������������������������������������������14 3.1. Benefits measured by the CyclingMax tool��������������������������������������������������������������������������������������� 15 3.2. Input and default parameters used by the tool�������������������������������������������������������������������������������� 15 3.3. Key outputs����������������������������������������������������������������������������������������������������������������������������������������������� 16 Chapter 4. Case Studies�������������������������������������������������������������������������������������������������������������������������������� 17 4.1. Abidjan, Cote d’Ivoire������������������������������������������������������������������������������������������������������������������������������20 4.2. Dodoma, Tanzania�����������������������������������������������������������������������������������������������������������������������������������22 4.3. Kampala, Uganda������������������������������������������������������������������������������������������������������������������������������������24 4.4. Addis Ababa, Ethiopia�����������������������������������������������������������������������������������������������������������������������������26 4.5. Lima, Peru������������������������������������������������������������������������������������������������������������������������������������������������� 30 4.6. São Paulo, Brazil���������������������������������������������������������������������������������������������������������������������������������������32 4.7. Foz do Rio Itajaí Region, Brazil��������������������������������������������������������������������������������������������������������������34 4.8. Recife, Brazil���������������������������������������������������������������������������������������������������������������������������������������������36 Chapter 5. Inside the CyclingMax Tool������������������������������������������������������������������������������������������������������ 38 5.1. Cycling demand modeling����������������������������������������������������������������������������������������������������������������������40 5.2. Benefit modeling�������������������������������������������������������������������������������������������������������������������������������������� 41 5.3. Cost-benefit cashflow metrics�������������������������������������������������������������������������������������������������������������48 5.4. Modules in the tool�����������������������������������������������������������������������������������������������������������������������������������48 5.5. The future of CyclingMax�����������������������������������������������������������������������������������������������������������������������53 Appendix: Parameter Values and Sources������������������������������������������������������������������������������������������������ 55 References������������������������������������������������������������������������������������������������������������������������������������������������������ 60 Image Credits������������������������������������������������������������������������������������������������������������������������������������������������� 63 The Case for Cycling Infrastructure Investments iv List of Tables Table 2.1. Suggested CMFs for Cycling Lanes����������������������������������������������������������������������������������������������� 9 Table 4.1. Price of carbon for the estimation of environmental benefits���������������������������������������������46 Table A1. General Parameters������������������������������������������������������������������������������������������������������������������������55 Table A2. Accident Prevention Parameters�������������������������������������������������������������������������������������������������56 Table A3. Health Benefit Parameters�����������������������������������������������������������������������������������������������������������57 Table A4. Emission reduction parameters�������������������������������������������������������������������������������������������������� 58 Table A5. Time Savings Parameters�������������������������������������������������������������������������������������������������������������59 List of Figures Figure 2.1. Cycling travel, per-kilometer cyclist casualties, and kilometers of cycling infrastructure in Copenhagen������������������������������������������������������������������������������������������ 8 Figure 5.1. Cost of cycling lane per kilometer�����������������������������������������������������������������������������������������������39 Figure 5.2. Approach for forecasting cycling demand�������������������������������������������������������������������������������� 41 Figure 5.3. High-level structure of the World Bank CyclingMax tool������������������������������������������������������49 Figure 5.4. Landing page of the World Bank CyclingMax tool����������������������������������������������������������������� 50 Figure 5.5. Introduction page of the World Bank CyclingMax tool��������������������������������������������������������� 50 Figure 5.6. Image of the input module, which is the second interface encountered by the user when accessing the tool.������������������������������������������������������������������������������������������ 51 Figure 5.7. Interface for the advanced scenario where users can define the values of the input parameters�����������������������������������������������������������������������������������������������������������������52 Figure 5.8. Output of the CyclingMax tool�����������������������������������������������������������������������������������������������������53 The Case for Cycling Infrastructure Investments v Acknowledgments This report was jointly prepared by the Institute for Transportation and Development Policy (ITDP) and World Bank. The ITDP team was led by Dana Yanocha, Senior Research Manager, and Jacob Mason, Senior Director, Global Program. The World Bank team was led by Georges Darido, Lead Urban Transport Specialist; Winnie Wang, Lead Infrastructure Specialist, Ana Waksberg Guerrini, Senior Urban Transport Specialist; and Kazuyuki Neki, Transport Analyst. The team thanks the following people for reviewing the report: Felipe Targa (Senior Transport Specialist, ILCT1), Fatima Arroyo Arroyo (Senior Urban Transport Specialist, IAWT4), and Joanna Mclean Masic (Lead Urban Specialist, SURDR). Special thanks to Aiga Stokenberga, Javier Morales Sarriera, Sam William Johnson and Arif Uddin from the World Bank, and John Shauri, Gashaw Aberra, Chris Kost from ITDP Africa team, for providing detailed information for the case studies. The team is immensely grateful for technical support from Yuma Saito (Intern, ITRGK) and Jaeeun Lee (Intern, INFCE). Special thanks to Taylor Reich and Madeline Liberman from ITDP for their contributions to and review of the tool's methodology; Antônio Henrique from the City of Recife for providing detailed data for the case study; and Kees van Ommeren and Sibren Vegter (DECISIO Consulting, Netherlands). This report was developed with guidance from Nicolas Peltier (Director, Transport Global Practice), Binyam Reja (Practice Manager, Transport Global Knowledge Unit) and Heather Thompson (CEO, ITDP). Jonathan Davidar (Senior Knowledge Management and Learning Officer) oversaw the editing and visualization of the report. The report was edited and designed by RRD GO Creative. This work was supported by the Global Facility to Decarbonize Transport (GFDT) with a contribution from the Government of the Netherlands. 1 Introduction Explore how CyclingMax, the World Bank’s Cost-Benefit Tool for cycling facilities, can help planners and policymakers make informed decisions on building sustainable urban transport infrastructure. Explore use cases and benefits, identify inputs required, and get clear, quantifiable evidence of investment value. The Case for Cycling Infrastructure Investments 2 As cities around the world continue to expand rapidly, urban planners and policymakers are confronting the realities of urban mobility in densely populated areas. With rising incomes and urbanization rates, there is a noticeable increase in vehicle ownership, demanding more road space and significant funding for road and vehicle infrastructure maintenance. This trend, driven by an implicit subsidy of private motorized transport, discourages low-carbon and cost-effective travel modes like walking, cycling, and public transport. Such developments run counter to the urgent need for reducing emissions and fostering equitable and livable urban environments. Cities that promote active mobility enjoy lower emissions, better air quality, and healthier residents. Projections indicate that urban travel by walking, cycling, and public transport will drop 40 percent by 2050, causing greenhouse gas (GHG) emissions to surge 33 percent above current levels. On the other hand, according to the same study, a car-centric transport system costs 50 percent more in transport spending by governments and individuals compared to a system based on walking, cycling, and public transport. Governments could save up to 20 percent on transport budgets, although this percentage may vary by country. This estimate does not account for additional savings from reduced healthcare expenses and increased economic productivity, which could lead to even greater savings. Research shows that walking 30 minutes or cycling 20 minutes daily reduces mortality risk by at least 10 percent (WHO 2022). Low and Middle Income Country (LMIC) cities have a distinct opportunity to capitalize on their still low motorization rates and the high proportion of trips made by walking and cycling, also referred to as active mobility or non-motorized transport (NMT). Many cities in the Global South joined global networks, actively pursuing cycling as a key strategy to improve air pollution and reduce emissions from the transport sector; improve physical health; and boost the use of sustainable transport across the network. Despite this progress, most LMIC cities continue to experience declining rates of walking, cycling, and public transport use while motorization increases. To reverse this trend and maximize their existing advantages, cities must enhance the safety and quality of active mobility infrastructure. By doing so, they can ensure that residents choose walking and cycling out of preference rather than necessity (Pojani and Stead 2015). Research from UC-Davis and the Institute for Transportation and Development Policy (ITDP) highlights the massive gap in cycling infrastructure – cities currently build only one-tenth of what they need to meet global climate targets. With 68 percent of the world’s population projected to live in cities by 2050, it is critical to invest in infrastructure that supports sustainable transport modes. Strategic investments in cycling infrastructure offer a proven solution that transforms urban mobility while delivering substantial socioeconomic returns. The Case for Cycling Infrastructure Investments 3 1.1. Tackling barriers to financing active mobility infrastructure In response to this looming urban transport crisis, active mobility has gained prominence in both international and local policy agendas. Policymakers now recognize walking and cycling as essential components of safe, healthy, and green transport. This recognition extends to major global frameworks like the Sustainable Development Goals, Global Climate Agenda, and Global Health Agenda. The report “The Path Less Travelled: Scaling Up Active Mobility to Capture Economic and Climate Benefits” (World Bank, 2023) identifies opportunities to replicate large-scale cycle infrastructure investments, including in cities like Tianjin, China where investments in walking and cycling led to increased metro ridership (and revenues). It also points to the need for a standardized methodology for evaluating the costs and benefits of active mobility projects. The absence of reliable data and tools to assess the real benefits of cycling infrastructure has made it challenging to assess the return on investment. This lack of clarity limits the ability of decision-makers and investors to make well-informed impactful choices. Developing a data-driven tool would help build stakeholder capacity to invest and reduce the risk of scaling up active mobility initiatives. There is a growing body of evidence supporting the case for the economic and climate benefits of investing in cycling infrastructure. In 2021, the World Bank, along with the Government of the Netherlands and the World Resources Institute (WRI), published “Investing for Momentum in Active Mobility”, which compiled general evidence of the costs and benefits of investing in walking and cycling, as well as opportunities for funding and financing such investments. Similarly, in 2022, ITDP published “Making the Economic Case for Walking and Cycling” and “Protected Bicycle Lanes Protect the Climate”. Building on these foundational studies and recognizing the need for instruments that aid practical implementation, the new groundbreaking Cost-Benefit Analysis Tool for Cycling Facilities, also known as CyclingMax, was created in partnership with ITDP and Progress Analytics LLC. This innovative tool is designed to provide decision-makers with actionable insights using just a few simple inputs. It takes the guesswork out of estimating benefits and empowers smarter investments in cycling projects that will deliver long-term benefits for community health, sustainability, and quality of life. 1.2. Empowering policymakers to make informed decisions The objective of this project is to present a practical interactive tool to estimate the economic returns of cycling investments, particularly in developing regions where transport data is scarce. The CyclingMax tool combines user-friendly design with comprehensive analysis capabilities. Through its web-based interface, users can either work with built-in default values or input their own data to generate customized assessments. The tool calculates two key indicators which provide decision-makers with clear, quantifiable, and comparable estimates of investment value: • Net Present Value (NPV) • Economic Internal Rate of Return (EIRR) The Case for Cycling Infrastructure Investments 4 A comprehensive, standardized methodology offers the basis for more comparable and reliable estimations across projects of different sizes and in different locations. The tool is particularly useful for early-stage planning and low-data environments. The tool considers a wide spectrum of potential benefits, including: • Mobility impacts as measured by travel times • Road safety improvements • Public health gains • Environmental benefits These benefits are converted into monetized annual cash flows for clear comparison. On the cost side, the tool accounts for both initial construction expenses and ongoing maintenance requirements of cycling facilities. 1.3. Creating a pathway to optimize cycling infrastructure investments The CyclingMax tool allows policymakers and investors to quickly evaluate potential cycling investments in early stages of project planning, from basic infrastructure improvements to protected bike lane networks. It also provides insights to critical questions, such as: • What are the critical factors that can influence the socioeconomic returns of cycling infrastructure investments? • How can a standardized cost-benefit methodology support decisions to scale up active mobility investments? Overall, this approach ensures that decision-makers can quickly assess socioeconomic returns while considering the full range of benefits that cycling infrastructure brings to urban communities. 2 The Case for Cycling Infrastructure Examine the multiple benefits of urban cycling infrastructure worldwide, backed by evidence from existing research on its impacts. This section establishes the positive impact of cycling networks on safety, sustainable personal mobility, health, and the environment to transform cities and enhance urban mobility. The Case for Cycling Infrastructure Investments 6 Protected/segregated1 cycling lanes provide a safe and efficient environment for cycling traffic. This is proven by multiple studies which show that such facilities encourage more people to cycle. Increased cycling use can lead to less reliance on motorized vehicles, which in turn reduces traffic congestion and the associated environmental impacts. Additionally, cycling facilities can improve public health by promoting physical activity, reducing transportation costs for individuals, and creating a more livable urban environment through reductions in noise and improvements in air quality. A NETWORK OF PROTECTED BIKEWAYS CREATES A SAFER CYCLING EXPERIENCE AND INCREASES RIDERSHIP What is a protected bikeway? What is NOT a protected bikeway? A lane for people on bicycles that is Bicycle lanes that are not physically physically separated from pedestrians No clear separated from vehicles or pedestrians, and vehicle traffic, often by a curb, Physically including painted lanes, shared traffic impact on bollards, parked cars, or planters separate lanes, and shared sidewalks road safety cyclists from pedestrians and vehicles Increase bicycle trips, especially among women No clear impact on bicycle ridership Save users time Expose cyclists and money to vehicles encroaching, parking in, or turning across the bikeway Reduce Improve pedestrian cyclist and Reduce injuries comfort pedestrian and fatalities for comfort all road users Protected bikeways are one of the best ways to get more people on bicycles for more trips MYTHS AND REALITIES OF PROTECTED BIKEWAYS *Cycling lanes (bike lanes) described in the right-hand side are so called “dedicated” cycling lanes – separated only by lane markings on the roadway. MYTH Source: ITDP website https://itdp.org/multimedia/protected-bikeways-infographic-itdp/ MYTH The benefits derived Protected bikeways from cycling are cause more traffic not enough to by reducing the justify the financial Protected/segregated cycle lanes also contribute to safer roads for all users, because they can space for cars investment reduce the likelihood of crashes involving cyclists and motor vehicles. Recognizing the benefits MYTH of protected/segregated Protected cycle lanes, considerable REALITY research has been conducted to quantify the REALITY When protected More cycling leads to various aspects using a variety benefits fromexpensive bikeways are of models. This chapter provides bikeways are available, improved ahealth, reviewfewer of the major categories of benefits: safety, emissions, health, users of other modes and shift to cycling, reducing travel time. traffic injuries/fatalities, reduced emissions and congestion for people air pollution, shorter and who have to drive more affordable trips, REALITY and reduced congestion There are many for drivers design options that are effective and also cost-efficient, such as temporary or quick- build bikeways REALITY Protected bikeways Off-road dedicated facility: completely separated from motorized traffic 1 attract more diverse MYTH REALITY MYTH riders—women, Protected bikeways Cyclists spend more Protected bikeways children, and older reduce profits for per month at local are a waste of space adults; people from local businesses, and businesses than because they are different incomes and are a bad investment car drivers and only used by young, ethnicities—to cycle for cities promote more active physically-fit men cityscapes The Case for Cycling Infrastructure Investments 7 2.1. Safety benefits of well-planned cycling facilities Well-designed cycling facilities can substantially improve safety. Data from the city of Copenhagen has demonstrated that the construction of cycle lanes (which physically separate cyclists from higher speed vehicles) is associated with reduced rates of fatalities and injuries (Figure 2.1). Cycling facilities can improve safety in two major ways: 1. By inducing a shift from driving to cycling, thereby reducing motorized vehicle crashes 2. By improving the safety of existing cyclists (for example, those who were previously riding with motor vehicles on existing roads without cycling facilities) Protected/segregated cycling lanes can improve safety by providing safeguarded spaces for cyclists, reducing the likelihood of crashes with motor vehicles, and encouraging safer and more predictable interactions between cyclists and drivers. By separating cyclists from motorized vehicles, cycling lanes can also reduce the exposure of cyclists to road hazards and improve overall traffic safety for all road users. The Australian Transport Assessment and Planning Guidelines uses the difference between the baseline crash rate (the crash rate without the cycling project) and the crash rate after the installation of the cycling facility to estimate the reduction in crashes. A more commonly used approach is the Crash Modification Factor (CMF), which is the ratio of the crash rate with the safety improvement to the crash rate without the safety improvement. In the case of a cycling facility, the CMF represents the crash rate ratio of the newly constructed cycling facility to the existing traffic lane. A CMF smaller than 1 indicates a lower crash rate after the installation of the cycling facility. The Case for Cycling Infrastructure Investments 8 Figure 2.1. Cycling travel, per-kilometer cyclist casualties, and kilometers of cycling infrastructure in Copenhagen 1.2 1.0 Bic cl kilom tr s tr v l (w kd ) C clist s rious injuri s nd f t liti s p r km 0.3 380 388 397 411 348 367 323 338 1996 1998 2000 2002 2004 2006 2008 2010 C clin tr cks C clin l n s Gr n C cl rout s Source: OECD/International Transport Forum (2013), Cycling, Health and Safety, OECD Publishing/ITF. Properly designed cycling lanes have been found to reduce fatalities by 25 to 40 percent.Ref-i Here, properly designed cycling lanes mean those that are safe and efficient for cyclists in terms of better design and management of intersections, roadsides, midblock, special treatment for vulnerable road users, as well as speed management and traffic calming devices. Data from Bogota shows that despite an increase in bicycle use from 0.2 percent (2000) to 7 percent (2019), the city saw 34 percent fewer cycle-related deaths and 8 percent fewer injuries. New cyclists induced to use the new facility — referred to as induced cycling — could contribute to increasing the crash rate, particularly in unprotected lanes. For example, high traffic volume could increase interactions and conflicts among cyclists, leading to more collisions. In the absence of in depth studies in LMICs, the following CMFs (Table 2.1) are suggested based on studies from the United States and Australia, which are also adapted in the World Bank’s Transport Good Practice requirement assessment model.2 2 In October 2019, the World Bank launched a Good Practice Note (GPN) to address road safety. This GPN provides guidance to  World Bank staff on how to support efforts to improve road safety on projects supported by Investment Project Financing (IPF) and thereby meet the requirements of the ESF road safety standards (ESS4). To support the use of the GPN, the World Bank Transport GP has developed a ‘Road Safety Screening and Appraisal Tool (RSSAT), which is a tool to identify road safety performance and screen for opportunities for improvement in road and roadside infrastructure. https://thedocs.worldbank.org/en/ doc/648681570135612401-0290022019/Good-Practice-Note-Road-Safety The Case for Cycling Infrastructure Investments 9 Table 2.1. Suggested CMFs for Cycling Lanes Type of cycling facility CMF (base = none) Segregated cycling path with barrier (or separated from other traffic) 0.41 Non-protected dedicated cycling lane on the roadway (marking only) 0.82 None 1.00 Source: World Bank. For induced cycling traffic, the crash reduction is calculated based on the mode shift from motorized vehicles to bicycles. The safety benefit is calculated based on the reduction in crashes due to both existing and induced cyclists, including road crashes involved in other road users such as motorized vehicles and pedestrians. Thus, the individual crash risk should be decreased. Overall, the safety benefit of cycling facilities is well-documented. However, existing CMFs are primarily based on high-income countries. While these factors provide a good starting point, future research on CMFs and other coefficients for LMICs can improve the estimation accuracy. The current CyclingMax tool incorporates the safety benefits of both induced and existing cycling traffic. 2.2. Positive environmental impacts of increased cycling infrastructure The reduced reliance on motorized vehicles resulting from cycling facilities directly reduces the emissions of GHGs and air pollution. The Health Economic Assessment Tool (HEAT) for walking and cycling developed by the World Health Organization (WHO)Ref-ii calculates the differences in carbon emissions between cycling and other modes of transport across three categories: Operational emissions, which are determined by analyzing changes in travel demand, energy 1.  efficiency, and carbon intensity of the energy consumed. Energy supply emissions, which cover upstream emissions from the extraction, production, 2.  generation, and distribution of energy supplies, including emissions from fossil fuels and electric sources. Vehicle lifecycle emissions, which come from the manufacturing processes of vehicles and 3.  are based on aggregate carbon values for each vehicle type, considering factors like typical lifetime mileages, body mass weights, material composition, and material-specific emission and energy use. The monetary impact is calculated based on the Social Cost of Carbon (SCC), which represents the estimated present discounted value of present and future economic damage from emitting one ton of CO2 into the atmosphere today. ITDP has created a model to estimate the climate impacts of installing protected cycling lanes.Ref-iii This model calculates potential reductions in CO2 emissions based on the local population size adjacent to protected cycling lanes and incorporates a user-specified percentage for mode shift to bicycles from other forms of transportation. The environmental benefits are quantified as a The Case for Cycling Infrastructure Investments 10 reduction in tons of CO2 per annum, considering that bicycle travel does not emit CO2 compared to other transportation modes, such as private vehicles, which do. Furthermore, the ITDP tool incorporates essential data such as regional emission factors and the person-kilometers traveled within specific areas. The calculations of environmental benefits in the Australian Transport Assessment and Planning ToolRef-iv and the California Active Transportation Benefit-Cost Tool reflect the reductions in emissions and energy consumption from the reduced vehicle-distances traveled by motorized vehicles. Cycling facilities can induce demand for cycling and incentivize existing motorized vehicle users to shift to cycling. The above review showcases the complexity and significance of the emissions-reduction benefits of active mobility infrastructure. Sophisticated models such as HEAT consider the lifecycle and energy supply emissions of vehicles, requiring extensive information as input. The targeted users of the current tool typically do not have such extensive information. In addition, tools such as HEAT, which are intended for city- or country-level benefit evaluation, do not align with the scope of the current tool (project-level evaluation). As such, the CyclingMax tool adopts a relatively straightforward approach based on reduced vehicle distance coupled with emission factors. 2.3. Improved health and mortality rates for cyclists Active mobility such as cycling involves physical activity that can significantly improve the health of the cyclist. Regular cycling enhances cardiovascular fitness, strengthens muscles, improves joint mobility, and decreases stress levels. By incorporating cycling into daily routines, individuals can achieve substantial health improvements that contribute to longer life expectancy and overall well-being. A systematic review indicates that active commuting by walking or cycling decreased all-cause mortality by 9 percent and cardiovascular mortality by 15 percent.Ref-v Well-designed cycling lane infrastructure would thus induce additional cycling traffic to reduce mortality. Multiple studies have considered the health benefits of cycling lanes. The HEAT model developed by the WHO comprehensively evaluates the effects of cycling facilities on mortality from three aspects. The physical activity benefit describes the positive impact of choosing active transportation modes such as cycling. It is calculated by considering the local mortality rate and the duration of cycling activity. The benefit is reflected in the reduction in all-cause mortality. The HEAT model uses a coefficient of 0.9, indicating a 10 percent lower mortality rate for cyclists compared with non-cyclists. According to a report published by the World Bank and ITDP, health savings are the largest monetized benefit of cycling infrastructure in Buenos Aires, Argentina and the second largest in Lima, Peru, highlighting the importance of the health benefits of cycling facilities.Ref-vi Similarly, the benefit assessment in the Australian Transport Assessment and Planning Guidelines considers the increased physical activity from cycling, which leads to improved health outcomes and reduced healthcare costs.Ref-vii The Australian model uses public health data and existing studies to quantify physical activity levels and determine health benefits. The California Active Transportation Benefit-Cost ToolRef-viii calculates the reduction in mortality risk based on the reduction in mortality rate resulting from additional cycling-related exercise and the original all-cause mortality rate in the area. The Case for Cycling Infrastructure Investments 11 While cycling in general is associated with positive effects, the WHO HEAT tool also includes two negative impacts. Air pollution risk is a negative effect stemming from cyclists’ exposure to local PM2.5 concentrations. Opting for cycling as a mode of transportation can increase pollution-related mortality risk among cyclists. The extent of this increased risk is determined by factors including the local PM2.5 levels, cycling duration, the ventilation rate of the cyclist, and various adjustment parameters. The second negative effect is associated with crashes and is addressed under the safety benefit category. As most existing studies only consider the benefit of cycling facilities in terms of reduced mortality, the current CyclingMax tool focuses on this aspect. One of the key parameters for accurately estimating the health benefit is the annual reduction in mortality. For example, the CyclingMax tool uses a 4.5 percent annual reduction in mortality for cycling facilities in the United States as suggested by the CALTRAN model.Ref-ix This rate is expected to vary by country and region. Accordingly, the CyclingMax webtool provides reference rates for other countries and regions that can be selected by the user. These mortality reduction rates were derived from existing studies, as shown in Table A3 in Appendix. 2.4. Reduced travel time Because protected cycle lanes shift trips away from private vehicles, travel time reduction can occur. Various studies have captured the time-saving benefits of cycling facilities. For example, case studies indicated that active mobility investments saved travelers 15 minutes per metro trip and 2 to 4 minutes per bus trip in Tianjin, China and amounted to travel-time savings equivalent to USD2.6 billion in Lima, Peru.Ref-vi The calculation of time-saving benefits appears to be simple. For example, the Australian Transport Assessment and Planning Guidelines calculate the time saved by cyclists after the implementation of a cycling project by measuring the difference in travel time before and after the project is built.Ref-iv This time saving is then valued using the Value of Time, which assigns a monetary value to time based on average wages and other societal measures: Time Saving Benefit = Number of Trips × Time Saved per Trip × Value of Time. The main challenge in this calculation lies in the accurate estimation of the number of trips and time saved per trip (the current tool estimates the demand as the total cycling time). The time saved per trip depends heavily on the local transit system and motor vehicle infrastructure. Such information typically requires a detailed examination of multiple factors, including the waiting time for transit, connection time, location of parking facilities, and walking distance to and from the parking facilities to final destinations. For this reason, the travel benefit in terms of time saving is included as an advanced benefit calculation in the CyclingMax tool due to the difficulty in identifying default parameter values. Advanced users who have the expertise and resources to accurately estimate the related parameters can opt to include this benefit. The Case for Cycling Infrastructure Investments 12 2.5. Broader impacts in other categories Several other benefit categories in literature were reviewed by the study team. These were not included in the CyclingMax tool for the following reasons: Journey quality improvement: A relatively subjective measure that requires a preference matrix 1)  from the users to define the preferred index of different cycling facilities (cycling lane, cycling way, cycling path, etc.). The California tool includes the calculation of this benefit. However, this calculation is only applicable in situations where multiple types of cycling facilities will be built, and each type of facility has an existing and quantified preference level in the local community. Thus, it does not apply to the situations where the CyclingMax tool will be used. Air pollution benefits: Generally calculated in two categories: lifecycle emissions for vehicles 2)  and the emission cost for all pollutants (CO, NOx, PM2.5, and SOx) in the area. The life-cycle emission calculation involves operational emissions of all modes of travel, energy supply emissions (from the extraction, production, generation, and distribution of energy supplies), and vehicle lifecycle emissions (the emissions for manufacturing and disposing the vehicles). The WHO HEAT model includes this calculation based on an embedded database of lifecycle emissions for different modes (cars, trains, buses, etc.). The CALTRAN tool calculates the benefit of emission cost savings for all pollutants because they have the cost data of all the pollutants. The CyclingMax tool does not include these two calculation categories because both involve extensive input parameters. The parameters from other locations are typically not transferrable – they are location specific and vary significantly from area to area. The absenteeism benefit: This can be calculated as the decrease in the number of sick days 3)  resulting from the mode shift to cycling and the subsequent increase in exercise. This benefit is calculated by the California tool as a function of the following parameters: average absenteeism of employees, percentage covered by short-term sick leave, percentage of sick days reduced when active at least 30 minutes per day, value of reduced absenteeism per day, and cycling days per year. Due to the many uncertainties in these parameters, this benefit was excluded from the CyclingMax tool. Intersection safety improvement: This can be calculated based on the effects of adding 4)  cyclist-friendly features at intersections, as done in the California tool. This calculation applies mainly to cycling facility improvement projects where intersection improvement counter measures are specified (traffic signal for cyclists, stop bar for cyclists, or markers on the ground for cyclists, etc.) and the corresponding effects are well quantified. Due to the fact that such sophisticated data are highly unlikely available in developing countries, this benefit is not included in the CyclingMax tool. Impact on local economic development and retail activity: A review of studies on the impacts of 5)  local economy indicates that creating or improving active travel facilities generally has positive or non-significant economic impacts on retail and food service businesses located nearby. There could be negative economic effects on businesses that are auto-centric. The quantification of impact of local economic requires detailed site-specific data and need to be considered for future extension of the tool. Decongestion: The calculation method for this factor is straightforward, but requires 6)  considerable efforts to validate the input parameters, which include the benefit of decongestion ($/km). This parameter is provided by the users in the Australian tool. It is difficult to validate The Case for Cycling Infrastructure Investments 13 without a comprehensive traffic study that confirms the existing number of motor vehicle trips, a car ownership survey, and a detailed traffic fundamental diagram (with locally calibrated parameters including density, velocity, and traffic flow). Other benefits may be added to the tool in the future if more studies are performed to validate the parameters needed to accurately calculate the benefits. For example: • Cycling lanes induce more public transit trips, which stimulate local business, and more cycling trips will help cycling-related business. • The operational costs (VOC) for cyclists are significantly lower compared to those for car drivers. Therefore, switching from cars to bicycles can lead to substantial savings in terms of depreciation, insurance, parking costs, fuel, and other expenses. • Increased accessibility to cycling lanes. These benefits require additional information to supply the necessary input parameters and are not included in the CyclingMax tool currently. 2.6. Key takeaways The chapter showcases the complexity of cost-benefit analysis for cycling facilities, which can be summarized as follows: • Benefits for society can be difficult to recognize or monetize: The societal benefits of cycling projects, such as improved health outcomes, reduced environmental impacts, and enhanced quality of life, can be challenging to quantify and assign a monetary value. These benefits often accrue over time and may not be immediately apparent, making it difficult to capture their full impact in traditional cost-benefit analyses. • Applications at the project level are limited: Cost-benefit analyses are often conducted at the city or country level. There is limited literature on comprehensive cost-benefit analyses at the project level. As a result, reference parameters and the associated methods are scarce. • The costs and benefits can vary substantially based on the location of the project: The financial costs and benefits associated with a project may differ greatly depending on its geographical location. Factors such as local economic conditions, population density, existing infrastructure, and environmental conditions can all influence the outcomes of a cost-benefit analysis, leading to significant variability in results. • Studies using the typical cost-benefit framework with standard metrics are limited: Few cost-benefit analyses of cycling facilities have been conducted using standardized frameworks and metrics such as the Economic Internal Rate of Return (EIRR) and Net Present Value (NPV). The lack of consistent methodologies and metrics makes it challenging to compare and evaluate the outcomes of different projects accurately. • A user-friendly tool to facilitate benefit estimation is currently lacking: There is a notable absence of accessible and easy-to-use tools designed to assist in estimating the benefits of projects. This makes it difficult for practitioners and decision-makers to conduct comprehensive benefit analyses, potentially leading to underestimation or misrepresentation of a project’s true value. The CyclingMax tool is intended to address or mitigate some of these limitations by providing a user-friendly, flexible, and expandable webtool that is based on solid methodology. 3 Getting Started with the CyclingMax Tool Understand how the CyclingMax tool works to optimally evaluate cycling infrastructure investments. This section gives insight into the components, data requirements, calculation processes, and analytical approaches to understand how the tool generates its cost-benefit assessments. The Case for Cycling Infrastructure Investments 15 The CyclingMax tool is meant to evaluate new cycling infrastructure projects that create a network of dedicated cycling lanes. While the tool can assess upgrades to existing cycling facilities, users should carefully adjust input parameters, as the values for new construction differ significantly from those for improvement projects. 3.1. Benefits measured by the CyclingMax tool The CyclingMax tool provides an accounting of the benefits and costs of proposed cycling facilities, giving decision-makers an aggregated view of the positive effects of cycling infrastructure. Here, the focus is on those that can be reasonably estimated based on available research and data and that demonstrate opportunities for future benefits. The CyclingMax includes four benefit categories: • Safety. CyclingMax calculates safety benefits for both traffic shifted from cars and existing cycling traffic, accounting for improved safety resulting from cycling lanes that provide exclusive access to cyclists with road safety features. The benefit from crashes avoided by car riders switching to cycling is estimated from the average cost of car crashes. Existing cyclists who travel in existing facilities (for example, the roadway with no cycle lane or unprotected cycle lane before the cycling facility is installed) in mixed traffic with cars will also benefit. This benefit is assessed using Crash Modification Factors and the average cost of bicycle crashes. • Emissions. The emissions benefit is calculated from the reduction in CO2 from the mode shift from cars to cycling. The CyclingMax extracts emission costs ($/g) from a lookup table based on World Bank data that extends to 2050. • Health. The health benefit is calculated as the reduction in mortality due to increased exercise. Physical activity associated with cycling will lead to improved health and reduced mortality. The cost savings are estimated based on the value of a statistical life (VSL), which is defined as how much individuals are willing to pay for a very small reduction in the probability of death. • Travel time savings. When calculating savings in travel time, the tool considers both time savings for traffic shifted from walking and additional time costs for traffic shifted from cars and public transit. Given that this tool was designed for use in developing countries, travelers will likely be switching from walking to cycling, resulting in travel time saving benefits. There are ongoing discussions on benefits of travel time savings due to mode shift from car to cycling. While mode shift from car to cycling typically leads to longer travel times, recent meta-analysis on value of travel time savings (VTTS) in developing countries suggest that VTTS for cycling and walking might be smaller. This means that the perceived “negative benefit” of increased travel time could be offset by a much greater willingness to spend time cycling, potentially making it a significant benefit overall. Thus, to avoid overestimation of the benefits of travel time savings, the current tool focuses on the benefits of mode shift from walking to cycling. 3.2. Input and default parameters used by the tool The benefits and costs of a cycling facility depend strongly on the location. For example, the cost of crashes and the value of statistical life can vary dramatically from country to country. In addition to a comprehensive consideration of benefits from cycling facility construction, the CyclingMax tool incorporates flexible and customizable settings for key parameters. Users can input specific parameter values based on the infrastructure under consideration and the local area. The Case for Cycling Infrastructure Investments 16 The user can also opt to use the tool’s default parameter values. For example, for time-varying parameters that depend on per capita Gross Domestic Product (GDP) — such as VSL — the tool extracts the most recent value from the World Bank using an API to ensure the calculation is up to date. For location-specific parameters, the tool provides reference default values from published studies. Users can customize any coefficients based on their own research. CyclingMax also allows administrators to add, modify, and delete reference values, allowing for future expansion of the tool. As the analysis is primarily for new construction, the parameters should be carefully calibrated when used for facility improvements. For example, the percentage of induced cycling traffic and construction cost could differ substantially between new construction and facility improvements. 3.3. Key outputs Importantly, the CyclingMax tool outputs monetized metrics — this includes the total costs of construction and maintenance as well as annual benefits in the four benefit categories over the project evaluation period (multiple decades in the future or the number of years selected by the users). The tool also calculates the Net Present Value (NPV) and the Economic Internal Rate of Return (EIRR), two key metrics for cost-benefit analysis. These tangible and actionable outputs allow users to immediately grasp the cost-benefit of a project and make informed decisions about the economic viability of the investment. 4 Case Studies This chapter helps determine the effectiveness of the Cycling Max tool by applying it to real-world instances. Insights from eight low- and middle-income cities provides data that can positively impact investment and policy. The Case for Cycling Infrastructure Investments 18 In this section, the CyclingMax tool is applied to eight planned or in progress cycle lane or facility projects in low- and middle-income cities. The value and projected returns on investment of cycling infrastructure varies significantly due to project quality and design, existing modal splits, implementation and maintenance costs, and more. Some projects have been completed, others have estimation costs. However, the eight case studies included in this report provide EIRRs well above the minimum required for viability. The EIRRs are greater than the discount rate used in the case studies, which range from 6.0-12.0 percent. In addition, the projects show positive net present values (NPVs) as seen in this summary table. Investment ($US) EIRR NPV ($US) Abidjan, Cote d’Ivoire $6 million 123.5% $52 million Dodoma, Tanzania $27 million 41.6% $60 million Kampala, Uganda $131 million 55.8% $1.08 billion Addis Ababa, Ethiopia $118 million 75.7% $689 million Lima, Peru $17.4 million 85.7% $144 million Sao Paulo, Brazil $18.7 million 88.6% $156 million Itajai, Brazil $37 million 44.3% $148 million Recife, Brazil $55.5 million 91.5% $594 million The Case for Cycling Infrastructure Investments 19 In addition to EIRR and NPV outputs, results for each of the four key benefit areas are provided for every case: • SAFETY | Impact of the cycle lane or facility on fatal and serious crashes per year • HEALTH | Impact of the cycle lane or facility on mortality (due to physical activity) per year • EMISSIONS | Impact of the cycle lane or facility on carbon dioxide emissions per year • TRAVEL TIME | Impact of the cycle lane or facility on travel time per year Cycle Lane Safety Health Emissions Travel time KMs Crashes Mortality CO2 Million hours prevented/ prevented/ reduced/ saved/year year year year Abidjan, Cote d’Ivoire 15 5 12 280 tons 0.7 Dodoma, Tanzania 105 16 7 425 tons 4.8 Kampala, Uganda 493 96 309 5,732 tons 24.4 Addis Ababa, Ethiopia 677 257 405 7,541 tons 15.6 Lima, Peru 50 9 9 1,078 tons 0.3 Sao Paulo, Brazil 14 8 3 654 tons 0.08 Itajai, Brazil 95 7 8 1,632 tons 0.2 Recife, Brazil 156 22 24 5,147 tons 1.2 As discussed in Chapter 2.5, due to data limitations, the CyclingMax tool does not quantify all potential benefits from cycle infrastructure networks. This means that the results are likely an underestimation of benefits. Conversely, observed benefits may be less than projected if designs are altered and the cycle infrastructure is not fully protected when implemented. The Case for Cycling Infrastructure Investments 20 4.1. Abidj n, Cot d'Ivoir M ss tr nsit nd ctiv mobilit f ciliti s to cr t r li bl public tr nsport n tworks Cit St ts 6.3 million popul tion Abidj n, th r pidl rowin conomic hub of Côt d'Ivoir , is hom to mor th n h lf of th countr ’s popul tion. D spit si nific nt public inv stm nts in ro d infr structur in r c nt rs, th cit stru l s with unr li bl urb n tr nsport, hi h tr nsport costs nd incr sin con stion, which und rmin its comp titiv n ss. 2119 sq km r 13.6 million d il trips Ch ll n s Whil public tr nsport ccounts for 80 p rc nt of ll motori d trips in th cit , th d m nd is l r l m t b inform l s rvic s th t r oft n in ffici nt nd costl for 15 km 164,703 us rs. From 1998 to 2013, th sh r of form l public tr nsport d cr s d b mor th n 50 p rc nt. Conv rs l , th inform l Tot l l n th Popul tion in s ctor, comprisin Gb k , m t r d t xis, woro-woro nd int r-commun l t xis, incr s d its sh r from 68 p rc nt of cov r r public tr nsport trips in 1998 to ov r 85 p rc nt in 2013. Cons qu ntl , si nific nt portion of Abidj n’s popul tion continu s to r l on w lkin for th ir d il mobilit n ds. Approxim t l 40 p rc nt of th 13.6 million d il trips in th cit r m d on foot. This fi ur ris s to 60 p rc nt in conomic ll vuln r bl r s, du to th poor qu lit nd un fford bilit of th public tr nsport s st m. $6 MN Estim t d Proj ct Ov rvi w construction cost of c cl To support its rowin popul tion nd conom , Abidj n’s urb n mobilit r quir s subst nti l improv m nts, p rticul rl in m ss l n s tr nsit nd ctiv mobilit . Thos int rv ntions will improv quit bl cc ss to jobs nd oth r s rvic s for vuln r bl popul tions, includin wom n nd p opl livin in pov rt , whil lso ddr ssin issu s such s con stions, ro d s f t , ir pollution nd r nhous s missions. The Case for Cycling Infrastructure Investments 21 M p 4.1. Popul tion d nsit , r li nc on w lkin , nd pl nn d m ss tr nsit lin s Popul tion d nsit p r km Sh r of w lkin in ov r ll trips ndin in th on Sourc : SDUGA, PACOGA. As p rt of bro d r initi tiv to improv urb n mobilit conditions of Abidj n, n pproxim t l 20km of E st-W st BRT corridor will b built b tw n th conomic ll vuln r bl r s of Yopou on nd Bin rvill . C cl l n s will lso b impl m nt d to nh nc first nd l st mil cc ssibilit of th BRT s rvic , imin to ncour s f multimod l trips. Th infr structur d si n will im to prioriti c clists nd p d stri ns, b r -purposin ro d sp c s from v hicl tr ffic l n s to prot ct d c cl l n s nd sid w lks. R sults Th C clin M x tool r sults for th multimod l corridors nd w lkin nd c clin n twork pl nn d for Abidj n includ : EIRR NPV 123.5 % US$ 52 MN SAFETY HEALTH Numb r of Numb r of r duc d pr v nt d f t l mort liti s throu h nd s rious incr s d ph sic l cr sh s p r r ctiviti s p r r 4.7 11.5 EMISSIONS TRAVEL TIME CO2 missions Tr v l tim s v d p r r p r r 280 tons 739,849 hours void d The Case for Cycling Infrastructure Investments 22 4.2. Dodom , T n ni Multimod l corridors nd clim t -r sili nt ctiv mobilit n tworks for r li bl tr nsport F cin 6.4 p rc nt nnu l popul tion rowth, Dodom ’s tr nsport Cit St ts s st m is in ur nt n d of up r d s. Mor th n 80 p rc nt of th cit ’s ro ds r still unp v d, m kin it difficult for m n r sid nts 765,179 Cit ’s to tr v l r li bl . Th cit n ds r sili nt ro d infr structur to r sid nts in 2022 built-up r r duc vuln r bilit to clim t imp cts. To pl n for th futur , xp ndin b th cit mb rk d on n int r t d tr nsport proj ct to prioriti s f , sust in bl mobilit throu h low- mission options nd ctiv mor th n tr nsport. 440 p rc nt From 11 km² in 2000 to Ch ll n s 60 km² in 2024 Clim t Emissions Ro d In ffici nc Inclusion of r sili nc nd s f t in public vuln r bl pollutions tr nsport p opl Compon nt 1: Proj ct Ov rvi w Multimod l Corridors Th Dodom Int r t d nd Sust in bl Tr nsport Proj ct ims to t ckl th s ch ll n s b impl m ntin compl t str ts, includin c cl l n s, which contribut to sust in bl , inclusiv , nd ffici nt urb n mobilit . This proj ct cov rs two compon nts: to cr t multimod l corridors nd clim t -r sili nt w lkin nd c clin n twork. 45.1 km 127,690 Tot l l n th Popul tion in cov r r Th proj ct will up r d four rt ri l ro ds th t conn ct Dodom ’s cit c nt r to oth r p rts of th countr . Th s ro ds, which curr ntl h v two l n s, will b xp nd d to includ s p r t sp c s for diff r nt us rs: c rs, bus s, p d stri ns, nd c clists. Ei ht p rc nt of th n w ro d sp c will b us d for c cl l n s. Th ro ds will m t int rn tion l s f t st nd rds $131.3 MN $7 MN (thr -st r iRAP r tin ). On ch sid of th s ro ds, th r will b 5 m t rs of sp c for w lkin nd c clin , pl c d t th out r Tot l cost of th Estim t d d s. This l out k ps p d stri ns nd c clists s f from tr ffic compon nt construction nd pr v nts futur buildin into th ro d sp c . Th ro ds will b cc ssibl to v r on , with f tur s lik slop d curbs nd cost of c cl t xtur d p v m nts to h lp p opl with dis biliti s l n s n vi t s f l . The Case for Cycling Infrastructure Investments 23 Fi ur 4.1. Ex mpl s ction of multimod l corridor 60000 9000 9000 1500 1500 0 300 300 500 500 0 112 50 50 112 0 30 0 30 00 32 50 50 32 00 Motor v hicl c rri w s 14 14 00 W rin cours , Bind r cours , 50 00 Bituminous B s , Gr nul r B s , Subb s L r, 0 50 0 Fillin : If r quir d 14 50 142 0 2 140 00 S rvic ro ds 14 50 W rin cours , Gr nul r B s , Subb s L r Fillin : If r quir d 0 250 250 0 C clin l n s 3950 W rin cours , Gr nul r B s , Subb s L r 3950 Fillin : If r quir d Footp ths P vin Block, S nd B ddin , B s Sourc : Ministr of Works, T n ni N tion l Ro d A nc . Compon nt 2: Clim t -r sili nt w lkin nd c clin n twork Tot l l n th: Popul tion in Tot l cost of th Estim t d construction ~60 km cov r r : sub-compon nt: cost of c cl l n s: 81,018 $15.2 million $20 million N w w lkin nd c clin p ths will b built lon xistin ro ds nd throu h p rks nd r n r s. Th s p ths will fill in missin links in th curr nt n twork, m kin it si r for p opl to w lk or c cl s f l . This is sp ci ll import nt in bus r s wh r w lkin f ciliti s do not curr ntl xist. Th proj ct will dd: R sults • W lkw s • Li htin Th C clin M x tool r sults for th multimod l corridors nd w lkin nd c clin n twork • Dr in s st ms pl nn d for Dodom includ : • N w ro d surf c s wh r n d d • C cl l n s wh r sp c p rmits • S f t improv m nts in cr sh-pron r s EIRR NPV 41.6 % US$ 60 MN Wh n buildin p ths throu h r n sp c s, th proj ct will includ : • W lkin nd c clin p ths SAFETY HEALTH • Li htin Numb r of Numb r of r duc d • S f dr in ch nn ls pr v nt d f t l mort liti s throu h • L ndsc pin nd s rious incr s d ph sic l • Sm ll str ts (no wid r th n 3 m t rs) cr sh s p r r ctiviti s p r r • D dic t d crossin p ths 16 7 • F tur s to slow down tr ffic t int rs ctions EMISSIONS TRAVEL TIME All p ths will b d si n d for s of us , CO2 missions Tr v l tim s v d with slop d curbs nd t xtur d p v m nts to p r r p r r h lp p opl with diff rin biliti s to njo th infr structur . 426 tons 4,813,121 hours void d The Case for Cycling Infrastructure Investments 24 4.3. K mp l , U nd Cit St ts C clin in th Gr t r K mp l M tropolit n Ar (GKMA) ccounts for onl 2 p rc nt of trips. W lkin is 1000 sq km r th prim r mod of tr nsport, m kin up 46 p rc nt of trips. In surv conduct d in 2021, r sid nts not d th t fford bl bic cl s, prot ct d c cl tr cks, nd s f c cl p rkin would improv th c clin nvironm nt in K mp l . 3.65 million Th K mp l C cl N twork Pl n (2023-2032) outlin s popul tion compr h nsiv str t to d v lop c clin ( xp ct d to r ch infr structur in th GKMA, which includ s th c pit l 5 million b 2030) cit of K mp l nd fiv surroundin municip liti s, ddr ssin th rowin popul tion’s mobilit n ds mid incr sin con stion nd pollution. It mph si s th import nc of w ll-conn ct d c cl n twork for promotin sust in bl tr nsport, improvin public h lth, nd r ducin missions. Th pl n id ntifi s c clin s k str t to r duc tr v l costs. It lso 5.6 p rc nt nnu l r co ni s th n d for compl m nt r m sur s such popul tion rowth r t s bik sh r s st m, s cur bic cl p rkin , nd c r-fr on s to support cc ssibl , s f c clin for mor r sid nts. V rious c clist roups nd th ir sp cific n ds r lso not d in th pl n with c lls for quit bl cc ss to c clin f ciliti s nd hi hli htin th conomic, nvironm nt l, nd s f t b n fits of c clin . Ch ll n s GKMA f c s s v r l ch ll n s in impl m ntin its 493 km 3.37 MN c cl n twork pl n succ ssfull : Tot l l n th Popul tion in Ro d s f t is k ch ll n , sp ci ll in th K mp l cit c nt r wh r v hicl cr sh s r cov r r conc ntr t d. Motorc cl t xis, known s bod bod s, r wid l us d, with 200,000 op r tin cross th m tropolit n r . Bod bod s contribut to hi h r t of cr sh s, oft n sp din nd viol tin tr ffic l ws. Post d sp d limits xc d 40 kph on m n downtown str ts, contributin to cr sh s th t r sult in d th nd s rious injur . $131 MN Anoth r ch ll n is nd r inclusion. Thou h bout 50 p rc nt of wom n in K mp l us public Estim t d tr nsport to commut to work, it c n b si nific nt construction cost, nd th qu lit of v hicl s nd ro ds is poor. cost of c cl Wom n curr ntl ccount for onl 0.04 p rc nt of c clists. l n s Th r ion’s topo r ph m pr s nt ddition l ch ll n s for c clists, with 35 p rc nt of str ts in K mp l h vin r di nts bov 3 p rc nt ( n r ll cc pt d thr shold for lon r c clin trips). The Case for Cycling Infrastructure Investments 25 Proj ct Ov rvi w Th K mp l C cl N twork Pl n propos s n twork of 493 km of prot ct d c cl l n s divid d cross thr ph s s. K mp l curr ntl h s 2 km of c cl l n s. Ph s 1 Ph s 2 Ph s 3 Includ s 84 km nd will Will tot l 166 km, m kin C cl l n s will im to focus on ddin l n s conn ctions b tw n Ph s 1 rout s conn ct th n twork lon rout s with hi h nd prioriti in l n s lon hi h to comm rci l r s c clist counts nd to cr sh-risk corridors id ntifi d in th nd oth r m jor s rv s f d rs to K mp l Ro d S f t R port. L n s corridors, tot lin BRT corridors. will lso b pl nn d to li n with th 241 km. K mp l Cit Ro ds nd Brid s Up r d Proj ct. R sults Th C clin M x tool w s ppli d to th full K mp l C cl N twork Pl n, which includ s 493 km of prot ct d c cl l n s. EIRR NPV 55.79 % US$ 1,082 MN SAFETY HEALTH Numb r of Numb r of r duc d pr v nt d f t l mort liti s throu h nd s rious incr s d ph sic l cr sh s p r r ctiviti s p r r 96.5 309.4 EMISSIONS TRAVEL TIME CO2 missions Tr v l tim s v d p r r p r r 5,732.9 tons 24,396,127.62 void d hours The Case for Cycling Infrastructure Investments 26 4.4. Addis Ab b , Ethiopi Compr h nsiv c clin infr structur to nh nc urb n mobilit Cit St ts Addis Ab b is hom to 5.4 million p opl , or 25 p rc nt of 527 sq km Ethiopi ’s urb n popul tion, nd is mon th f st st- rowin r urb n r s in th world. About 54 p rc nt of r sid nts w lk nd 5.4 million 31 p rc nt us public tr nsport for th ir d il trips. Acc ss to popul tion quit bl , fford bl mobilit b c m n issu du to insuffici nt public tr nsport s rvic , poor tr ffic m n m nt, nd str t d si ns th t prioriti motori d v hicl s ov r p d stri ns. Ch ll n s In Addis Ab b , bic cl own rship is v r low, with bout 8 p rc nt of r sid nts r portin ownin bic cl (comp r d to 25 p rc nt 25 p rc nt who own c r). About h lf of th popul tion do not of Ethiopi ’s know how to c cl , nd onl bout 3 p rc nt of hous holds us urb n bic cl for tr nsport on w kl b sis. Surv s show th t popul tion c clin ccounts for b tw n 3-6 p rc nt of trips, prim ril mon low-incom m n. Encour in us of c cl l n s, supportin bic cl own rship nd providin th ri ht infr structur is n c ss r to c t l swift upt k of c clin . 677 km 2.7 MN Ro d s f t pos s m jor ch ll n , with hi h-risk cr sh s conc ntr t d on m jor ro ds with hi h l v ls of p d stri n nd c clist ctivit . Hi h v hicl sp ds, sp ci ll lon th urb n Tot l l n th Popul tion in xpr ssw wh r th r r f w d si n t d p d stri n cov r r crossin s, m k w lkin nd c clin p rticul rl d n rous. Proj ct Ov rvi w Th Addis Ab b C cl N twork Pl n (2023-2032) ims to cr t compr h nsiv n twork, tot lin 677 kilom t rs of c cl l n s to $118 MN nh nc urb n mobilit . Th pl n mph si s s f t nd cc ssibilit for div rs us r roups, sp ci ll wom n nd Estim t d childr n. Th impl m nt tion is structur d in thr ph s s: construction cost of c cl Tot l l n th: Popul tion in l n s 677 km cov r r : 2.7 million Estim t d construction cost of c cl l n s: $118 million The Case for Cycling Infrastructure Investments 27 Ph s 1 Ph s 2 Ph s 3 Includ s 144 km of l n s focus d Includ s 189 km of Compl t s th n twork with n ddition l 345 km, in hi h-d m nd r s to conn ct l n s, xp ndin nd int r tin c clin with bro d r urb n pl nnin initi tiv s. c clists to public tr nsport. improvin xistin C cl l n s will lso b incorpor t d lon m ss r pid tr nsit In ddition to up r din som rout s b s d on us r lin s. Th Addis Ab b Cit M st r Pl n propos d 15 BRT xistin c cl l n s nd ddin f db ck, nd nh ncin corridors to b impl m nt d ov r t n rs. An BRT corridor k corridors conn ctin th cit s f t b inst llin with width of mor th n 35 m sh ll includ c cl l n s. c nt r with out r r s, s r t d l n s. To f cilit t conv ni nt conn ctions b tw n st tions nd it includ s th l unch of ori ins of trips, f d r str ts int rs ctin th s m ss bik sh r s st m to incr s tr nsport corridors will b quipp d with c cl l n s or s f cc ss to bic cl s. Ph s 1 sh r d sp c s. Mor ov r, ll riv rsid proj cts r xp ct d to impl m nt tion b n in 2024 incorpor t c cl l n s to ncour c clin in th s r s. with n rl 50km of c cl tr cks compl t d. Throu hout th thr ph s s, c cl l n s in xistin str ts will b built with ph sic l s p r tion from mix d tr ffic. N w str ts will b built with l v t d c cl l n s t th Ph s 1. Short T rm ( rs 1-2) s m l v l s th footp th. M p 4.2. C cl l n n twork: Ph s 1 Tot l l n th: Popul tion in Estim t d construction 144 km cov r r cost of c cl l n s 937,332 $26.1 million The Case for Cycling Infrastructure Investments 28 Th short-t rm c cl n twork pl n includ s on oin bic cl proj cts, th first nd s cond ph s bik sh r cov r r , trunk corridors conn ctin th cit c nt r with p riph r l r s, nd up r d s to rli r c cl l n proj cts. Ph s 2. M dium T rm ( rs 3-5) M p 4.3. C cl l n n twork: Ph s 2 Tot l l n th: Popul tion in Estim t d construction 187.8 km cov r r cost of c cl l n s 866,740 $34.1 million This ph s will s 30 p rc nt of th c cl n twork impl m nt d, t r tin corridors within th s cond ph s cov r r of th bik sh r s st m, corridors th t r p rt of th riv rsid d v lopm nt proj cts, n w conn ctions b tw n th cit c nt r nd p riph r l r s, nd n tworks within condominium r s. The Case for Cycling Infrastructure Investments 29 Ph s 3. Lon T rm ( rs 6-10) M p 4.4. C cl l n n twork: Ph s 3 Tot l l n th: Popul tion in Estim t d construction 345.2 km cov r r cost of c cl l n s R sults 794,596 $118.4 million Th C clin M x tool r sults for th multimod l corridors nd w lkin nd c clin n twork pl nn d for Addis Ab b includ : In th lon t rm, prot ct d c clin l n s sh ll b provid d on ll m jor str ts with width of 30 m or mor . Th loc l str ts EIRR NPV sh ll b d si n d s sh r d str ts to ccommod t c clin , w lkin , nd slow-sp d v hicl mov m nt. C cl l n s sh ll b 75.7 % US$ 678.7 MN incorpor t d lon th m ss r pid tr nsit lin s. SAFETY HEALTH Th Addis Ab b Cit M st r Pl n propos d 15 BRT corridors Numb r of Numb r of r duc d to b impl m nt d ov r t n rs. An BRT corridor with pr v nt d f t l mort liti s throu h width of mor th n 35 m sh ll includ c cl l n s. To f cilit t nd s rious incr s d ph sic l conv ni nt conn ctions b tw n st tions nd ori ins of trips, cr sh s p r r ctiviti s p r r f d r str ts int rs ctin th s m ss tr nsport corridors will 257.25 404.55 b quipp d with c cl l n s or s f sh r d sp c s. Mor ov r, ll riv rsid proj cts r xp ct d to incorpor t c cl l n s to EMISSIONS TRAVEL TIME ncour c clin in th s r s. CO2 missions Tr v l tim s v d p r r p r r 7,541 tons 15,640,499 hours void d The Case for Cycling Infrastructure Investments 30 4.5. Lim , P ru Up r d d c clin infr structur n twork to incr s cc ss to jobs nd s rvic s Lim 's public tr nsport h s not k pt p c with its xp ndin Cit St ts popul tion. This hind rs cc ss to jobs nd s rvic s, sp ci ll for thos with low r incom s. Most r sid nts r l on public tr nsport — 10.9 million th l r l in ffici nt bus tr nsit s st m — with its si nific nt ps popul tion in s rvic . Th cit lso stru l s with: • H v tr ffic • Air pollution 2,672 sq km • Risin r nhous s missions r • Ro d s f t conc rns Th incr sin motori tion r t s h v l d to spik in c r 332 km of ccid nts, n tiv l imp ctin hum n c pit l nd productivit . disp r t c clin l n s Ch ll n s C clin in Lim h s rown slowl —from 0.3 p rc nt of ll trips in 2012 to 0.6 p rc nt in 2023. Whil th cit h s 332 kilom t rs of c cl l n s, th f c multipl issu s: Multimod l Corridors Th l n s don't M n r s Th l n s conn ct w ll to h v no c cl r n't built ch oth r l n s t ll w ll Int rs ctions r Onl thr bus Wom n 50 km 303,000 d n rous for c clists nd r pid tr nsit sp ci ll f l st tions h v bik uns f c clin Tot l l n th Popul tion in p d stri ns p rkin cov r r Th s probl ms show in th st tistics: p d stri ns nd c clists m k up 55 p rc nt of ll ro d d ths, mostl t int rs ctions. Proj ct Ov rvi w $38.7 MN $17.4 MN A World B nk stud in 2020 found th t Lim n ds 1,383 kilom t rs of prot ct d c cl l n s—much Tot l cost of th Estim t d mor th n it h s now. Th cit ’s curr nt bik l n compon nt construction pl n d t s b ck to 2005. Whil n w r studi s cost of c cl su st ddin 470 kilom t rs of l n s b 2040, Lim still do sn’t h v n up-to-d t , offici l pl n l n s th t xpl ins how to build nd conn ct ll th s c cl l n s. The Case for Cycling Infrastructure Investments 31 M p 4.5. B for nd ft r proj ct int rv ntions A conn ct d n twork of hi h-qu lit s r t d c cl l n s is b in impl m nt d cross 15 districts in c ntr l Lim s p rt of compr h nsiv “compl t str ts” int rv ntions. R sults Th n twork ims to improv tr v l conditions for p d stri ns nd c clists nd int r t non-motori d tr nsport (NMT) Th C clin M x tool r sults for th multimod l corridors nd w lkin nd c clin n twork with public tr nsport, th r b promotin mod l shift from pl nn d for Lim includ : motori d mod s. Th 50 km of priorit conn ctions h v b n id ntifi d usin crit ri such s th conn ctivit of th Lim C nt r n twork, with th o l of closin th ps EIRR NPV b tw n xistin prim r bik l n s. 85.7 % US$ 144 MN Clim t r sili nc is incorpor t d into th d si n of SAFETY HEALTH Numb r of Numb r of r duc d c cl l n s with th us of dur bl m t ri ls nd th pr v nt d f t l mort liti s throu h impl m nt tion of n tur -b s d solutions. Th proj ct nd s rious incr s d ph sic l lso includ s th pr p r tion of n in rin d si ns cr sh s p r r ctiviti s p r r for n ddition l 150 km of c cl l n s, nh nc m nts to s f t nd function lit t int rs ctions nd th 9 9 impl m nt tion of public bik sh r s rvic , which ims to b p rticul rl cc ssibl for wom n. EMISSIONS TRAVEL TIME CO2 missions Tr v l tim s v d p r r p r r 1,078.16 tons 302,982 hours void d The Case for Cycling Infrastructure Investments 32 4.6. S o P ulo, Br il BRT lin s nd s r t d c cl l n s to t ckl tr ffic con stion nd soci l issu s Cit St ts Th S o P ulo M tropolit n R ion (SPMR) is Br il’s most si nific nt conomic r , contributin ov r 20 p rc nt of th n tion’s GDP. How v r, r pid urb ni tion h s l d to uncontroll d urb n spr wl, x c rb tin soci l issu s. Within th SPMR, S o P ulo Cit xp ri nc s si nific nt 12 million soci l in qu lit , with 1.3 million of its 12 million r sid nts popul tion livin b low th int rn tion l pov rt lin . 5th l r st Ch ll n s m tropolit n 1.3 million Th cit ’s r pid motori tion h s m d it th fifth most r in th con st d cit lob ll . Tr ffic con stion is stim t d to r sid nts world b low cost th cit bout 8 p rc nt of th m tropolit n r ’s GDP in 2013, or mor th n p rc nt of Br il’s GDP. This is pov rt lin du to productivit loss s, GHG missions nd ir 1,493 sq km pollution, which is r sponsibl for pproxim t l 5,000 r pr m tur d ths nnu ll . D spit SPMR’s inv stm nts in public tr nsport ov r th p st d c d , includin m tro, suburb n r ilw s, nd bus s, th xistin bus n tworks r m in disconn ct d from oth r mod s nd r in ffici ntl op r t d. Addition ll , ro d s f t is si nific nt conc rn, with 850 d ths, or 6.56 d ths p r 100,000 p opl , ttribut d to ro d ccid nts nnu ll . Th inclusion of wom n nd vuln r bl popul tions in public tr nsport lso n ds subst nti l improv m nt. 14 km 92,114 Proj ct Ov rvi w Tot l l n th Popul tion in cov r r To ddr ss th s issu s compr h nsiv l , n w BRT corridors with s r t d c cl l n s runnin p r ll ll h v b n propos d b th Municip lit of S o P ulo (MSP). Th will nh nc cc ss to jobs nd s rvic s, p rticul rl for on of th cit ’s low st-incom nd most soci ll vuln r bl communiti s. This is m nt to imp ct 52 p rc nt of th popul tion, includin 29,000 hous holds in urb n slums, who f c hi h soci l vuln r bilit nd low cc ssibilit . $103.25 MN $18.7 MN Sinc bic cl s r n fford bl nd sust in bl mod of tr nsport fr qu ntl us d b wom n, thos s r t d Estim t d l n s will improv s f t , inclusion nd sust in bilit . Tot l cost of th compon nt construction cost of c cl l n s The Case for Cycling Infrastructure Investments 33 M p 4.6. C clin l n lon Aric nduv bus r pid tr nsit corridor, S o P ulo Sourc : World B nk. R sults Th s r t d bic cl l n s will b construct d throu hout th l n th of th S o P ulo Aric nduv Bus R pid Tr nsit Corridor. Th C clin M x tool r sults for th multimod l To ncour th us of non-motori d tr nsport, S o P ulo Cit corridors nd w lkin nd c clin n twork h s lr d d v lop d 506 km of th bic cl l n s. Accordin to pl nn d for S o P ulo includ : th Municip l Bic cl Pl n (2019), th bic cl l n n twork will b EIRR NPV xp nd d from 506 km to 1,800 km b 2028. 88.6 % US$ 156 MN Th bic cl l n lon th Aric nduv Corridor SAFETY HEALTH h s b n prioriti d in this pl n, b c us of citi n Numb r of Numb r of r duc d f db ck in public consult tions s w ll s b in pr v nt d f t l mort liti s throu h k pi c to nsur w ll-conn ct d bic cl nd s rious incr s d ph sic l n twork. Th pl nn d s r t d c cl l n s cr sh s p r r ctiviti s p r r could miti t ro d s f t risks for bic clists nd oth r mod s b r ducin conflict mon diff r nt 7.8 3.1 mod s. Som BRT st tions will h v bic cl EMISSIONS TRAVEL TIME p rkin f ciliti s — subj ct to v il bilit of CO2 missions Tr v l tim s v d sp c — to furth r promot ctiv mobilit . p r r p r r 653.91 tons 83,732 hours void d The Case for Cycling Infrastructure Investments 34 4.7. Fo do Rio It j í R ion, Br il Int r t d, sust in bl ctiv mobilit for r pidl rowin , tourist-fri ndl cit Cit St ts Th Fo do Rio It j í R ion h s 811,000 r-round r sid nts spr d cross 1,004 squ r kilom t rs. Its popul tion h s b n rowin quickl —3.6 p rc nt ch r from 2018 to 2023. 811,000 B 2030, bout 1.1 million p opl r xp ct d to liv h r r sid nts r-round r-round. Durin th bus tourist s son, th popul tion doubl s to mor th n 1.4 million p opl . Whil th r ion is n r ll w lthi r th n oth r p rts of th countr , th r is still hi h l v l of in qu lit . P opl with low r incom s oft n stru l to r ch jobs, m rk ts nd b sic s rvic s. 1.4 million popul tion Ch ll n s in tourist s son Th r ion’s 11 municip liti s r not w ll conn ct d b public tr nsport. This h s l d to s v r l probl ms: 1,004 sq km r Hi h Risin Poor ro d L ck of motori tion, pollution nd s f t inclusiv n ss with incr sin r nhous pr ctic s nd tow rds wom n d p nd nc on s l v ls infr structur nd p opl with priv t v hicl s low r incom s Compon nt 1: D dic t d bik p ths conn ctin th diff r nt municip liti s Proj ct Ov rvi w lon th BRT corridors Th r ion w nts to m k it si r nd s f r for p opl to w lk nd c cl to cc ss public tr nsport s w ll s conomic nd soci l opportuniti s. Buildin s f p ths nd l n s for w lkin nd c clin is sp ci ll import nt to h lp p opl from low r-incom n i hborhoods r ch th n w bus s st m th t’s b in pl nn d. This will h lp m k tr nsport tion mor quit bl for v r on . 70 km 100,000 Two BRT corridors nd four -bus corridors h v b n pl nn d. Th r d dic t d to conn ct jobs, tourist c nt rs, public s rvic s Tot l l n th Popul tion in nd n int rn tion l irport cross th 11 municip liti s in th r ion. Alon th n w BRT corridors, 70 km of d dic t d bik p ths cov r r will b impl m nt d or improv d, lon with sid w lks nd p d stri n f ciliti s. This int rv ntion M p 4.7. AMFRI R ion l mobilit p rticul rl ims to turn pl n’s vision for BRT n twork in trips b priv t v hicl into th Fo do Rio It j í R ion multimod l trips, with ctiv mobilit nd n w $3 MN $7.6 MN BRTs/ -bus s for commutin , which r Tot l cost of th Estim t d mor sust in bl nd compon nt construction ffici nt. As ctiv mobilit s rv s s th first nd l st cost of c cl mil , compl m ntin BRTs l n s nd -bus s, it will m k trips b this public tr nsport s st m mor Sourc : CIM-AMFRI—Corridors s d fin d ttr ctiv for us rs. b th Mobilit Pl n (2016). The Case for Cycling Infrastructure Investments 35 Compon nt 2: Activ mobilit corridors conn ctin conomic ll vuln r bl n i hborhoods Tot l l n th: Popul tion in Tot l cost of th Estim t d construction ~25 km cov r r : sub-compon nt: cost of c cl l n s: 130,000 $29.5 million $29.5 million Th n w str t d si n will lso h lp r duc th imp ct of s v r w th r. M p 4.8. Activ mobilit links to b improv d to conn ct Photo 4.1. B sic d si n b for nd ft r C minhos do M r th b ch r with th cit ’s low-incom r s int rv ntions Sourc : World B nk. Not : Th C minhos do M r propos l is nvision d in th m st r pl n—with th r ist r d f mili s of th PAB. Sourc : CIM-AMFRI/World B nk. Th proj ct will tr nsform str ts b tw n th conomic ll vuln r bl n i hborhood of B ln rio C mboriú's nd th w t rfront job r . Inst d of focusin R sults m inl on c rs, th s n w “compl t str ts” will m k it s f r for p opl to w lk nd c cl . Som str ts will iv Th C clin M x tool r sults for th multimod l priorit to p d stri ns nd c clists, with strict limits on corridors nd w lkin nd c clin n twork c r tr ffic nd sp d. Th d si n includ s: pl nn d for Fo do Rio It j í R ion includ : • S p r t l n s for c clists EIRR NPV • B tt r dr in to h ndl h v r in 44.3 % US$ 148 MN • Mor tr s nd pl nts lon th str ts SAFETY HEALTH • F tur s th t m k th ro ds s f r Numb r of Numb r of r duc d pr v nt d f t l mort liti s throu h nd s rious incr s d ph sic l Th s ch n s will m k it si r for v r on to r ch cr sh s p r r ctiviti s p r r jobs n r th w t rfront, sp ci ll : 6.8 7.7 • P opl from low r-incom r s EMISSIONS TRAVEL TIME • Wom n tr v lin lon CO2 missions Tr v l tim s v d • Childr n p r r p r r • Oth r vuln r bl roups 1,633 tons 209,071 hours void d The Case for Cycling Infrastructure Investments 36 4.8. R cif , Br il C clin m st rpl n to ncour ctiv mobilit nd curb individu l motori d tr ffic Cit St ts Th M tropolit n R ion of R cif , with popul tion of 3.7 million s of 2022, is th 8th l r st m tropolit n r in Br il. Th r ion is m d up of 14 municip liti s, with R cif b in th l r st – nd th fourth l r st cit in Br il. Simil r to m n oth r Br ili n r ions, its conomic, urb n, nd d mo r phic rowth h s l d to si nific nt ris in individu l motori d tr ffic. Th p rson l v hicl 218 sq km fl t r w b 115 p rc nt in th m tropolit n r ion nd b 1.6 million 85 p rc nt in R cif b tw n 2000 nd 2013. popul tion Cons qu ntl , th r ion d v lop d its c clin m st r pl n to ddr ss mobilit ch ll n s includin s v r con stion, ir pollution, nd c rbon missions. Th pl n r co ni s th pot nti l for c clin to b n fit individu l us rs, loc l communiti s, nd th cit ’s conom . In p rticul r, th pl n ims to int r t c clin with public tr nsport, ncour in th cc pt nc of c clin s r listic tr nsport mod for short trips nd promotin 8th l r st multimod l trips for lon r dist nc s. m tropolit n r in Br il Ch ll n s Thou h th r ion’s c cl n twork pl n is mbitious, ps Multimod l Corridors r m in. In R cif , r s th t r s riousl und rs rv d b tr nsport r not cov r d b th c cl l n n twork. C cl l n s r inst d conc ntr t d in hi h r-incom n i hborhoods, x c rb tin d p soci l in qu liti s th t xist in th cit . 156 km 725,154 M int n nc of xistin c cl l n s lso pos s ch ll n s limit d municip l fundin is m d v il bl for this. , Tot l l n th Popul tion in cov r r Proj ct Ov rvi w Th Cit of R cif h d b n dv ncin c clin v n b for th C clin M st rpl n w s d v lop d t th m tropolit n l v l in 2014. R cif ’s Tr nsport nd Mobilit M st r Pl n (2011) nd R d s Cicl v is r port (2010) r co ni d c clin s w to $55.5 MN n r t conomic, nvironm nt l, nd soci l b n fits for th cit . Th C clin M st rpl n cov rs th 14 citi s which compris th Estim t d R cif m tropolit n r , proposin c cl n twork of 591 km. construction Th pl nn d n twork is divid d into “m tropolit n” nd “suppl m nt l” n tworks. Th 245 km m tropolit n n twork is cost of c cl und r th St t ’s r sponsibilit nd 346 km of suppl m nt l l n s l n s r und r ch Cit ’s r sponsibilit . Within th Cit of R cif , th “m tropolit n” n twork ccounts for 71 km of prot ct d c clin infr structur . Th r r 178 km of “suppl m nt l” l n s, which includ s 156 km of prot ct d l n s, 4.2 km of ciclof ix ( d si n common in Br ili n citi s with sm ll r fl ctiv d lin tors lon th l n ), nd 18.4 km of unprot ct d l n s. The Case for Cycling Infrastructure Investments 37 Fi ur 4.2. Ex mpl of str t s ction with bidir ction l, s r t d c cl l n M p 4.9. C cl l n m p P ss io Pist d rol m nto d v iculos Ciclovi P ss io F ix d s ur nc C nt iro Min. Min. 0.5m 2.40m Livr Livr Sourc : T ctr n/idom, 2013. Sourc : T ctr n/idom, 2013. R sults R cif ’s c cl n twork pl n consists of lon -dist nc corridors nd short r dir ct rout s. For both t p s, Th C clin M x tool w s ppli d onl to th th r is p rticul r focus on conn ctin public 156 km of prot ct d c cl l n s th t m k up tr nsport tion hubs with th c cl l n n twork, th “suppl m nt l” n twork of loc l rout s to promot multimod l trips ov r usin priv t v hicl s. fund d nd m int in d b th Cit of R cif . Th m tropolit n corridors r m nt to promot int r-municip l trips within th bro d r m tropolit n EIRR NPV r ion. Whil m inl impl m nt d lon sid public tr nsport corridors, som str ts r propos d to b 91.52 % US$ 593.73 MN xclusiv l compos d of c cl l n s to furth r nh nc th s f t nd comfort of c clists. Th compl m nt r SAFETY HEALTH n twork, on th oth r h nd, ims to f cilit t trips Numb r of Numb r of r duc d within ch municip lit in th r ion. It conn cts th pr v nt d f t l mort liti s throu h m tropolit n n twork to public tr nsport t rmin ls nd nd s rious incr s d ph sic l oth r points of int r st lik univ rsiti s, schools, cr sh s p r r ctiviti s p r r shoppin m lls, mon oth rs. 21.55 24.29 EMISSIONS TRAVEL TIME CO2 missions Tr v l tim s v d p r r p r r 5,147.79 tons 1,246,608.45 void d hours 5 Inside the CyclingMax Tool Deep dive into the tool to understand its core components: from its mathematical models to its input and output modules. Learn how the tool processes diverse inputs to generate robust cost-benefit ratios and actionable investment insights. Understanding these mechanics helps planners and decision-makers better interpret results and customize analyses for their specific urban contexts. The Case for Cycling Infrastructure Investments 39 The cost of a cycling facility includes two major components: the initial construction cost incurred before the facility opens to traffic and the annual maintenance cost, which is incurred each year for maintenance since the facility opens to traffic. This construction cost can vary significantly according to the local costs of construction materials and labor. Several studies have surveys of the costs of cycle lanes, providing reference values for estimating cost. As users typically have an estimate of the project cost, the construction and maintenance costs are requested as inputs from the user in the input module. Figure 5.1 illustrates the cost of per kilometer for construction of cycle lane in a report by ITDP. Figure 5.1. Cost of cycling lane per kilometer C cl L n Costs p r Kilom t r, b T p nd R ion $48,000 / km Pl nt r - prot ct d $115,000 / km $128,000 / km l n S ttl , USA Curb - prot ct d c cl tr ck, Winnip , CAN Curb - prot ct d l n , Unit d St t s S ttl , USA $238,000 / km nd C n d Boll rd - prot ct d c cl tr ck, Toronto, CAN $108,000 / km $54,000 / km S r t d c cl tr ck, P rkin - prot ct d Br il l n México L tin Am ric $137,000 / km S r t d c cl tr ck, México $58,000 / km $176,000 / km C cl tr ck, EUR C cl tr ck with si n , $587,000 - $1.7 million / km Pol nd C cl hi hw , EUR $78,700 / km Europ D dic t d l n with boll rds, M rs ill s. FRA $32,000 / km Boll rds, B n kok, THL South st $47,000 / km Asi Boll rds, p v m nt m rkin s, M nil , PHL nd J k rt , IDO $175,000 / km $1.2 million / km B n luru, IND Compl t Str t, Indi Indi $89,000 / km Includ s int rs ction $33,000 / km improv m nts, C iro, EGP Addis Ab d , ETH $155,000 / km E st Afric Includ s int rs ction nd li htin improv m nts, B hir D r, ETH 30 60 90 120 150 180 210 240 270 300 330 360 Cost p r KM (000s USD) Unprot ct d Prot ct d (Low End) Prot ct d (Hi h End) Source: ITDP. (2022). Making the Economic Case for Cycling [online] Available from: https://www.mobiliseyourcity.net/sites/ default/files/2022-08/Making-the-Economic-Case-for-Cycling_6-13-22.pdf. The Case for Cycling Infrastructure Investments 40 5.1. Cycling demand modeling The demand for cycling traffic serves as critical input for assessing the benefits of cycling infrastructure. The volume of bicycle trips and their cumulative distance directly influence the benefits of a cycling facility, including the environmental, safety, and health benefits. Cycling demand is influenced by the location, type, and density of land use both along and within a specific radius of the bicycle facility. Various factors can lead to significant variations in cycling demand, including the following:Ref-x 1) Cycling facility type: cycling lane (with or without a physical divider between the cycling lane and the lane for motor vehicles), exclusive cycling lane, on-street cycling route, etc. 2) Existing transportation modes and demand 3) Existing local economic development and land use around the cycling facility Travel demand forecasting is well studied, and multiple methods for demand forecasting have been developed. In general, these demand forecasting models can be grouped into three general categories: • Trip-based four-step trip generation models. These models predict traffic demand based on a sequence of tasks that includes trip generation, trip distribution, mode choice, and route assignment. This is the industry standard for forecasting future demand. However, this method requires extensive input and complex modeling. The inputs require surveys, comprehensive coefficient selection, network development for trip distribution and route assignment, and sensitivity analysis. Thus, forecasting using the four-step model is typically carried out through dedicated consulting efforts for each project. • Activity-based travel demand models. These models improve upon the trip-based models by incorporating constraints related to time, space, and the linkages among activities and travel. Activity-based travel demand models have been increasingly adopted in recent years. • Strategic planning and sketching-planning models. These models are based on high-level estimates of trip rate per individual, population size, percentage of shift from other traffic modes, etc. Strategic planning and sketching-planning models typically require less information and less intensive modeling processes than trip- and activity-based models. Although trip- and activity-based models show potential for cycling demand forecasting, both modeling approaches require significant investment for data collection, traffic network construction, utility function development, and model calibration. The associated costs are often prohibitively high for cycling demand forecasting. Consequently, most cycling infrastructure cost-benefit analyses employ variations of strategic planning and sketch-planning models, which require less information and less burdensome modeling. However, as for trip- and activity-based models, the outcomes are sensitive to the chosen parameters. Therefore, identifying accurate parameter values is essential for precisely estimating cycling demand. Another challenge arises from the fact that the targeted users for a project may lack access to sources for the key parameters. Therefore, providing reasonable default values is critical. A suggested approach based on strategic-planning and sketch-planning models is illustrated in Figure 5.2. The Case for Cycling Infrastructure Investments 41 Figure 5.2. Approach for forecasting cycling demand Id ntif pot nti l bik d m nd mod ls c p bl G th r d f ult k of suppl in th s inputs. p r m t rs. Id ntif th n c ss r Id ntif th cruci l Appl th s l ct d bik inputs for bik d m nd p r m t rs r quir d for d m nd pr diction mod l s n l sis tools. bik d m nd mod lin . input for b n fit n l sis. Source: World Bank. The CyclingMax tool estimates demand based on the population affected along the new cycling facility. A simple linear regression is used to estimate the total induced travel distance resulting from the new cycling facility.Ref-xi Based on a sample of eight Latin American cities. This regression model (R2 = 0.88) concluded that for every person living within 300 m of a protected bicycle lane, roughly 315 km are cycled on protected lanes every year. Induced Biking Length = Population * 315 (km per year),(1) where Population is the population within 300 meters of the cycling facility. According to a study by the National Institutes of Health (NIH) installing new bicycle lanes will induce increases in bicycle use by 59 percent (trips) and 88 percent (total distance traveled) relative to the situation without bicycle lanes.Ref-xii Therefore, the existing cycling length is: Existing Biking Length = (km per year)(2) 5.2. Benefit modeling The CyclingMax tool includes four categories of benefits: safety, health, environmental, and travel time saving. During the development of the CyclingMax tool, several existing tools were reviewed. The CyclingMax incorporates the most valuable and project-relevant benefit categories from existing tools. Additional modeling modules were added to demonstrate these benefits effectively. Benefits requiring further research or parameters typically unavailable in developing countries were omitted. This section details the calculation methods for all benefits included in the CyclingMax tool and explains the rationale behind each analytical approach. Note that all the parameters/ variables discussed in this section are also listed in the Appendix. The reference number (ref #) of each parameter/variable indicated in the following sections is indexed in the Appendix for ease of identification. The rule of a half needs to be applied when assessing the impacts of induced traffic.Ref-xiii The Case for Cycling Infrastructure Investments 42 Safety benefits The CyclingMax tool considers the safety benefits of a cycling facility in two parts: 1) Benefit from shifting modes from cars to cycling. In existing cost and benefit analyses of cycling facilities, the safety benefits are typically calculated based on the amount of traffic that shifts from cars to cycling. The mode shift from car to cycling enhances safety by avoiding potential car crashes. The associated benefit is estimated from the average cost of crashes, crash rate, and the total amount of induced cycling distance that is diverted from car travel. The calculation formula is similar to those applied in the CALTRAN and Australia models.Ref-xiv Note that a single car is likely to have more than one occupant; thus, Vehicle Occupancy is included as a parameter in the calculation to reflect the total number of cars instead of total number of cycling riders: Safety Benefit from Mode Shift = Induced Cycling Length * (Trip Purpose Composition[1] + Trip Purpose Composition[2]) * Diversion from Cars / Vehicle Occupancy * Crash Rate * Average Serious Crash Cost * (Induced Benefit Factor),(3) where: • Induced Biking Length can be calculated from Equation (1) • Trip Purpose Composition[1] is the percentage of commuting in cycling traffic (ref 1) Trip Purpose Composition[2] is the percentage of cycling traffic other than commuting and recreational trips (ref 1). Following common safety benefit calculation practice, recreational trips were not included as recreational bike trips are elastic demand and may expose to less risk • Diversion from Cars is the percentage of newly induced cycling trips that were originally taken by cars (ref 15) • Vehicle Occupancy is the average number of people in each car (ref 3) • Crash Rate is the motor vehicle traffic crash rate per billion vehicle KM traveled (ref 7) • Induced Benefit Factor3 adjusts for the effects of unaccounted factors and is given a value of 0.5 • Average Serious Crash Cost is the cost per crash in USD (ref 5), which can be calculated as: Average Cost of Serious Crash = (pfatal* Cost per Fatal Crash + pinjury* Cost per Serious Injury Crash) / (pfatal + pinjury),(4) where: Cost per Fatal Crash = 70 * per capita GDP, and (5) Cost per Serious Injury Crash = 17.5 * per capita GDP(6) based on World Bank estimates,Ref-xiv and pfatal and pinjury are the proportions of fatal and serious injury crashes, respectively. Along with the calculation of burden of road crash in LMICs in the iRAP’s model, the safety benefit that will be calculated only includes fatal and serious injury based on a meta-analysis in LMICs. In the majority of situations, the calculation of the user benefit associated with induced traffic is relatively straightforward and  3 relies on the “rule of the half” methodology: P. Mackie et al. (2005). Treatment of Induced Traffic. [World Bank Transport Notes Series]. http://hdl.handle.net/10986/11796 The Case for Cycling Infrastructure Investments 43 The Global Road Safety Facility study suggested that the ratio of fatal to serious injury crashes is 1:15 at country level. However, as a logical assumption, this could vary by road infrastructure length. Suggested ratios could be: 1:2 for very short sections, 1:5 for short sections, 1:10 for medium-length sections, and 1:15 for long sections. The CyclingMax tool uses 1:15 as the default value, but users can adjust this ratio according to the specific project. Fatal crash rates per billion kilometers traveled by cars are available for limited counties. These data are only available for two developing counties: Mexico (27.5 fatal crashes per billion km traveled) and Malaysia (16.2 per billion km traveled). Most developed counties have low rates — between 3 and 9 fatal crashes per billion km traveled. The CyclingMax tool estimates the default fatal and serious injury crash rates as follows: a) The default fatal crash rate is set to 20 fatalities per billion km traveled by cars based on the average of the statistics available for Mexico and Malaysia. b) The estimated rate of fatal and serious-injury crashes is set at 16*20=320 per billion km traveled. The factor 16 comes from the 1:15 ratio of fatal to serious-injury crashes derived from World Bank research. 2) Benefit for existing cycling traffic. A second component of safety benefit (that is, the safety benefits of the cycling facility for existing cycling traffic) was incorporated into the CyclingMax tool in consideration of previous safety-related research conducted based on the Highway Safety Manual (HSM). This benefit reflects the reduction in cycling crashes in existing cycling traffic due to the newly built cycling facility. Similar to the calculation method of the HSM, the CyclingMax tool calculates this benefit based on the existing cycling distance, existing crash rate, average cost of cycling crashes, and CMF of the newly built cycling facility: Safety Benefit for Existing Cycling = Existing Cycling Length * Existing Cycling Crash Rate* (1-CMF) * Cost of Cycling Crashes,(7) where: • Existing Cycling Length is calculated using Equation (2) • Existing Bike Crash Rate refers to the crash rate between cycling and motor vehicles in mixed traffic conditions (ref 7) • Cost of Cycling Crashes is the average cost of crashes (ref 6) • CMF is the crash modification factor (ref 9), which ranges from 0.41 to 0.92 based on existing studies, implying a reduction of 59 percent to 8 percent in crash rate The World Bank has suggested the CMFs shown in Table 2.1, which are also adapted in the World Bank’s Transport GP assessment models. The Case for Cycling Infrastructure Investments 44 The fatal and serious injury cycling crash rate is a critical parameter when determining the safety benefit. Unfortunately, virtually all availably cycling crash rates are for developed countries, and no fatal and serious-injury crash rates are available, even for developed countries. We derived the default value for developing countries using the following logic: a) In United Kingdom, the fatal cycling crash rate is 36.8 per billion km traveled (23 per billion miles traveled) and fatal car crash rate is 4.8 per billion km traveled.Ref-xv,xvi b) The ratio of the rate of fatal cycling crashes to the rate of fatal car crashes is 36.8/4.8. c) The default value for the rate of fatal car crashes is 20 per billion km traveled, as discussed above in the “Benefit from shifting modes from cars to cycling” section. Assuming a constant ratio between the rates of fatal cycling crashes to car crashes, the fatal cycling crash rate should be 20*36.8/4.8=153 per billion km traveled. The corresponding fatal + serious injury crash rate should then be 16* 153=2,448 per billion km traveled. Health benefits Cycling facilities improve health by inducing exercise when users shift from car travel to bicycle travel. The calculation of health benefits in the CyclingMax tool involves the value of a statistical life, percentage of cycling (aged 16–64) in the population, percentage of induced cycling traffic, and the reduction in mortality due to exercise. The modeling method used combines features of the CALTRAN model and WHO HEAT model. However, instead of estimating the population affected by cycling exercise based on the estimated number of trips per traveler and average cycling distance of each trip, the CyclingMax tool asks users to provide the population as a direct input variable. This approach is more accurate and direct since the local population and the percentage of cyclists are both known parameters in most areas of the world; it is much more difficult to estimate the number of cycling trips and cycling distances. Health Benefit =Population within 300 meters of the cycling facility*Percentage of Cyclist in the Population*(Induced Cycling Length) / (Induced Biking Length +Existing Cycling Length)*Annual Reduction of Mortality *Allcause Mortality*Value ofa Statistical Life*(Induced Benefit Factor),(8) where: • Percentage of Cyclist in the Population is the percentage of the population aged 16–64 (ref 4) • Annual Reduction of Mortality is reduction in all-cause mortality due to cycling exercise (ref 11) • All-cause Mortality is the local mortality rate (ref 10) • Induced Benefit Factor: 0.5, which is a discount factor to adjust for the effect of unaccounted factors4 • Value of Statistical Life = 70 * per capita GDP (ref 12) 4 In the majority of situations, the calculation of the user benefit associated with induced traffic is relatively straightforward and  relies on the “rule of the half” methodology: P. Mackie et al. (2005). Treatment of Induced Traffic. [World Bank Transport Notes Series]. http://hdl.handle.net/10986/11796 The Case for Cycling Infrastructure Investments 45 Environmental benefits The CyclingMax tool calculates environmental benefits in terms of the amount of carbon dioxide that would have been used by cars if that amount of traffic did not switch from cars to cycling. The emission per car distance traveled is aggregated with the cost of emissions. The formula used to calculate the environmental benefit is similar to the method used in the CALTRAN model. However, rather than using a simple compound increasing rate to calculate the cost of emissions from year to year, CyclingMax uses a more accurate emission cost based on multiple previous studies with multiple years of data. The emission benefit in CyclingMax is calculated as: Emission Benefit = (Induced Biking Length)*(Trip Purpose Composition[1]+ Trip Purpose Composition[2])*Diversion from Cars / Vehicle Occupancy* (Emission Cost * Vehicle Emission Rate) * (Induced Benefit Factor)(9) where: • Trip Purpose Composition[1] is the percentage of commuting in cycling traffic (ref 1) Trip Purpose Composition[2] is the percentage of cycling traffic other than commuting and recreational trips (ref 1). Following common environmental benefit calculation practice, recreational trips were not included by default. For example, the Australian model does not include recreational trips, while California allows users to choose whether they should be included, which is likely due to the elastic nature of recreational bike demand. • Diversion from Cars is the percentage of newly induced cycling trips that were originally taken in cars (ref 15) • Vehicle Emission Rate is the parameter (ref 14) • Induced Benefit Factor: 0.5, which is a discount factor to adjust for the effect of unaccounted factorsRef-viii • Emission Cost can be found in the lookup table (Table 4.1 below) from the World Bank, which provides lower and upper bounds of dollar per tonnage for present until 2050. Based on these data, the carbon cost is set to between US$40 and $80 in 2020 and increases to US$50 to $100 by 2030. The Case for Cycling Infrastructure Investments 46 Table 4.1. Price of carbon for the estimation of environmental benefits Year Lower Bound ($/ton) Upper Bound ($/ton) 2022 42 84 2023 43 86 2024 44 87 2025 45 89 2026 46 91 2027 47 94 2028 48 96 2029 49 98 2030 50 100 2031 51 102 2032 52 105 2033 53 107 2034 55 109 2035 56 112 2036 57 114 2036 58 117 2038 60 120 2039 61 122 2040 63 125 2041 64 128 2042 65 131 2043 67 134 2044 68 137 2045 70 140 2046 71 143 2047 73 146 2048 75 149 2049 76 153 2050 78 156 Note: The price adjustment using the Consumer Price Index (CPI) involves recalculating the shadow price of carbon from a past year to reflect current prices may be needed in case the inflation is extensive. Source: World Bank. (2017). Shadow price of carbon in economic analysis. [Guidance note]. https://thedocs.worldbank.org/en/ doc/911381516303509498-0020022018/original/2017ShadowPriceofCarbonGuidanceNoteFINALCLEARED.pdf The Case for Cycling Infrastructure Investments 47 Travel time savings benefits The CyclingMax tool considers travel time savings derived from a traveler switching from walking to cycling. The tool also considers increases in travel time resulting from mode shifts from cars or public transit to cycling. The travel time savings is calculated as the sum of all changes in travel time resulting from diversions from cars, walking, and public transit to cycling. The diversion rates and average travel speeds of these modes are advanced parameters that must be input by users. The modeling method used in CyclingMax is modified from the M4 method,Ref-iv which calculates the travel time savings for existing cycling trips before and after a cycling facility is built. We believe that the time savings for such trips should not be significant if the travel distance is the same. In contrast, the difference in travel time resulting from switching to cycling from other modes will be significant given the different average travel speeds of these modes. Travel time savings (TTS) is calculated as follows: TTS = Value of Time * [(Induced Cycling Distance * Diversion Rate from Walk / Average Walk Speed – Induced Cycling Distance * Diversion Rate from Walk / Average Cycling Speed) + (Induced Cycling Distance * Diversion Rate from Car / Average Car Speed – Induced Cycling Distance * Diversion Rate from Car / Average Cycling Speed) + (Induced Cycling Distance *Diversion Rate from Transit / Average Transit Speed – Induced Cycling Distance * Diversion Rate from Transit / Average Cycling Speed)] * (Induced Benefit Factor),(10) where: • Induced Cycling Distance is the induced total cycling distance due to the newly built facility and can be calculated from Equation (1) • Diversion Rate from Cars is the percentage of newly induced cycling trips that were originally taken in cars (ref 15) • Diversion Rate from Walk is the percentage of newly induced cycling trips that were originally taken by walking (ref 15) • Diversion Rate from Transit is the percentage of newly induced cycling trips that were originally taken by walking (ref 15) • Average Cycling Speed is the average speed of cycling (km/h) (ref 16) • Average Car Speed is the average speed of driving (mph) including time spent on looking for parking, walking from parking to final destination, etc. (ref 16) • Average Transit Speed is the average speed of traveling by public transit including transfer and waiting time (km/h) (ref 16) • Induced Benefit Factor: 0.5, which is a discount factor to adjust for the effect of unaccounted factorsRef-xiii • Value of Time is calculated using Equation (11): Value of Time = e-4.191 * per capita GDP0.696(11) The Case for Cycling Infrastructure Investments 48 5.3. Cost-benefit cashflow metrics The tool calculates the annual cash flow based on the costs (for example, construction and maintenance costs) and monetized benefits, as illustrated in Figure 5. NPV is then calculated using the following equation: (12) where: • Cash Flown = Benefitn - Constructionn - Maintenance Costn. • EIRR is estimated by solving the following equation: , (13) where Ct is the cash flow at year t (not including the initial construction cost), and C0 is the initial construction cost. EIRR is the value when the NPV is equal to zero. 5.4. Modules in the tool The CyclingMax is an online tool that includes three primary modules (as shown in Figure 5.3): • Input Module • Background Calculation Module • Output Module The Case for Cycling Infrastructure Investments 49 Figure 5.3. High-level structure of the World Bank CyclingMax tool B n fit C lcul tion Construction Us r-Sp cifi d nd P r m t rs S f t M int n nc Cost H lth Int rn l Emission N t C sh Flow R turn R t Bik D m nd & & Estim tion Pr s nt-V lu N t Pr s nt C sh Flow V lu Tr v l Tim Ov r 20 rs Source: World Bank. Input module The input module (Figure 5.4) is the first interface that users encounter when accessing the tool. Users can select “Continue as a guest” or input login credentials. If users select “Continue as a guest”, the webtool will allow users to select default parameters from dropdown menus, or input customized parameters, and calculate the benefits. If users input login credentials as an administrator, the webtool will allow users to add input parameters to the dropdown menus as candidate parameters for future users. Following this page is the introduction page as shown in Figure 5.5. The users will be directed to the basic input information page after that (Figure 5.6). The input module requests three main inputs from the user: Select project location and input project name. The input module first asks the user to select 1)  a project location for the new cycling facility. The project location is used by the tool to identify default values for location-specific parameters required for the benefits calculation, including the per capita GDP, the value of time (VOT), value of statistical life (VSL), and the cost of crashes. The tool then extracts these parameters from an online database (Figure 5.6). Input basic project information. The input module requires the user to input basic information 2)  about the cycling facility (for example, the length of the facility, construction cost, maintenance cost, population, etc.). The data entered by the user in this section is used to estimate cycling demand. For now, the construction is assumed to be accomplished within one year before the project opens to traffic (Figure 5.6). The Case for Cycling Infrastructure Investments 50 Click on “Next Step” to enter the parameter input interface. Once the user clicked on the 3)  “NEXT STEP” button, the input module directs the user to a different interface (Figure 5.7) where they can define the values of the input parameters. This option empowers advanced users with more flexibility in determining the input variables. Figure 5.4. Landing page of the World Bank CyclingMax tool Source: World Bank. Figure 5.5. Introduction page of the World Bank CyclingMax tool Source: World Bank. The Case for Cycling Infrastructure Investments 51 Figure 5.6. Image of the input module, which is the second interface encountered by the user when accessing the tool. Source: World Bank. As shown in Figure 5.7, the input parameters included on the advanced scenario interface have pulldown menus with suggested values. The sources of the suggested values are listed in the Appendix. The sources of these suggested values are either existing cost and benefit analyses reported by various research institutes around the world or case studies conducted by the World Bank from different geographic locations worldwide. If these suggested values are not suitable for a specific project, CyclingMax allows users to input values for any parameter. Thus, if users choose to, they can specify the values for all the input parameters to best suit their local situation. Note that the number of available suggested values varies from parameter to parameter. Studies that comprehensively collect and evaluate all the parameters considered in the CyclingMax tool are very limited. The current parameter selections in the tool represent all the relevant parameters identified in our review of the literature. If future users wish to provide other suggested values, they can use the “Advanced scenario” option and/or update the dropdown menu to include other candidate parameters. The Case for Cycling Infrastructure Investments 52 Figure 5.7. Interface for the advanced scenario where users can define the values of the input parameters Source: World Bank. Output Module The CyclingMax tool calculates the annual cash flow associated with the cycling facility based on the cost, including both the construction and maintenance costs, and monetized benefits. The outputs (Figure 5.6) are provided as the net cash flow, present value cash flow, net present value (NPV), and economic internal rate of return (EIRR). Net cash flow is the difference between monetized benefits and cost by specific years. Present value cash flow is the current worth of a future cash flow discounted at a specific rate. NPV is the sum of the present values of incoming and outgoing cash flows over 20-year evaluation period. EIRR is the discount rate that makes the NPV of all cash flows from a particular project equal to zero. The EIRR and NPV provide a high-level summary of the overall benefit of the project. The Case for Cycling Infrastructure Investments 53 Figure 5.8. Output of the CyclingMax tool Source: World Bank. 5.5. The future of CyclingMax The CyclingMax tool is a straightforward tool that is readily available for use by users who may or may not have all the needed parameters to calculate benefits and costs of cycling facilities. It needs to be noted that there are several aspects that the tool can be improved in the future if more resources become available to improve the tool. This includes e-bikes as well as more sophisticated demand modeling method and sensitivity analysis to model the impacts of varied demands of cycling. A Appendix The Case for Cycling Infrastructure Investments 55 Appendix: Parameter Values and Sources Table A1. General Parameters Ref# Parameter Description Suggested Value Location Source 1 Trip Purpose The composition of [0.186, 0.353, CA, US Reviewed tools Composition the cycling traffic 0.461] (UCDAVIS and in [commute, CALTRAN) others, and [0.36, 0.61, 0.03] Argentina Case study recreational] (Buenos Aires in 2024) 2 Cycling Volume The trip growth 1.59% Multiple Case study Growth Rate rate due to the countries (Buenos Aires in newly built facility 2024) 6% Peru A study reviewed 11.5% Argentina Case study (Lima in 2023) 2% China WB ICR (Tianjin in 2023) 3 Vehicle The average 1.51 CA, US Reviewed tools Occupancy number of people (UCDAVIS and in each car CALTRAN) 4 % of population The percentage 54.9% CA, US Reviewed tools ages 16-64 of cyclists among (UCDAVIS and the population CALTRAN) involved Source: World Bank. The Case for Cycling Infrastructure Investments 56 Table A2. Accident Prevention Parameters Ref# Parameter Description Suggested Value Location Source 5 Average The average cost US $126,400 CA, US Reviewed tools Cost of Car per crash including (including all (UCDAVIS and Crashes fatal and serious crashes, including CALTRAN) injury crashes property damage only crashes) (70 * per capita Low- and World Bank, GRSF GDP + 17.5 * per middle-income capita GDP * 15) countries / 16 [in USD] 6 Average Cost The average cost $126,400 CA, US Reviewed tools of Cycling per crash including (UCDAVIS and Crashes fatal, injury, and CALTRAN) property-only crashes The average cost (70 * per capita per crash including GDP + 17.5 * per fatal and serious capita GDP * 15) injury crashes / 16 [in USD] 7 Crash Rate Default Fatal and 320 Developing See estimation Serious Injury countries on Methodology crash rate per chapter Safety billion km traveled Benefits section 8 Cycling Crash Fatal and Serious 2,448 Developing See estimation Rate injury crash rate countries on Methodology per billion km chapter Safety traveled. Benefits section 9 CMF Segregated cycling 0.41 Low- and World Bank, CMF path or physically middle-income memo protected on-road countries cycling lane Dedicated cycling 0.82 Low- and World Bank, CMF lane on roadway middle-income memo from no lane countries Crash modification 0.92 China WB ICR (Tianjin in factor from no 2023) build Source: The Case for Cycling Infrastructure Investments 57 Table A3. Health Benefit Parameters Ref# Parameter Description Suggested Value Location Source 10 All-cause The rate of all- 252 CA, US Reviewed tools mortality cause mortality (UCDAVIS and for cycling per 0.1 million CALTRAN) population people 446 India Reviewed tools (WHO, HEAT) 340 Argentina Case study (Buenos Aires in 2024) 11 Annual The reduced 4.5% CA, US Reviewed tools Reduction of percentage (UCDAVIS and Mortality of all-cause CALTRAN) mortality due to 21% France A Systematic exercise Review 5.2% Argentina Case study (Buenos Aires in 2024) 12 Value of The statistical 70 * per capita Low- and Statistical Life value of life GDP [in USD] middle-income countries Source: The Case for Cycling Infrastructure Investments 58 Table A4. Emission reduction parameters Ref# Parameter Description Suggested Value Location Source 13 Emission Cost The cost per ton Look up table Low- and of CO2 (Table 4.1. Price middle-income of carbon for the countries estimation of environmental benefits) 14 Vehicle The per-vehicle 207 [in g/km at CA, US (Model Reviewed tools Emission Rate CO2 emissions by 40km/h] 2024) (UCDAVIS and driving cars CALTRAN) 303 [in g/km] Peru Case study (Lima in 2023) 251 [in g/km] Argentina Case study (Buenos Aires in 2024) 294 USA ITDP PBLPC tool 167 Europe 155 China 100 India 151 Brazil 168 Other Americas 139 Africa 117 Other Europe Source: The Case for Cycling Infrastructure Investments 59 Table A5. Time Savings Parameters Ref# Parameter Description Suggested Value Location Source 15 Diversion From cars to 0.05 East Africa ITDP case studies Rates cycling (Dar es Salaam, Addis Ababa) 0.15 Argentina Case study (Buenos Aires in 2024) 0.36 Peru Case study (Lima in 2023) 0.5 CA, US Reviewed tools (UCDAVIS and CALTRAN) 0.29 China WB ICR (Tianjin in 2023) 0.049 Bogota ITDP PBLPC tool 0.016 Guangzhou ITDP PBLPC tool From walking 0.44 East Africa ITDP case studies to cycling (Dar es Salaam, Addis Ababa) 0.27 China WB ICR (Tianjin in 2023) 0.32 Bogota ITDP PBLPC tool 0.57 Guangzhou ITDP PBLPC tool From public 0.44 East Africa ITDP case studies transit to (Dar es Salaam, cycling Addis Ababa) 0.6 Argentina Case study (Buenos Aires in 2024) 0.64 Peru Case study (Lima in 2023) 16 Average The average (14,5.3,40) CA, US Reviewed tools Speed speed of [in km/h] (UCDAVIS and (Cycling, different CALTRAN) Walk, Car) modes (14,5.3, --) Argentina Case study (Buenos [in km/h] Aires in 2024) (16.5,3.6,-) Peru Case study [in km/h] (Lima in 2023) (22.3,--, --) China WB ICR [in km/h] (Tianjin in 2023) 17 Value of General cost of e-4.191 Low- and World Bank, Time time/cost for * per capita middle-income Meta-analysis of the business trips GDP0.696 countries value of time [in USD/hour] Source: The Case for Cycling Infrastructure Investments 60 References Ref-i iRAP. 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Protected Bicycle Lanes Protect the Climate Tool: https://itdp.org/multimedia/the-compact-city-scenario/ Reported road casualties Great Britain, annual report: 2022- Table 5. https://www.gov.uk/ government/statistics/reported-road-casualties-great-britain-annual-report-2022/reported-road- casualties-great-britain-annual-report-2022#casualties-and-rates-by-road-user-type Republic of Peru Lima Traffic Management and Sustainable Transport MPA, Item 67 “Increasing the modal share of bicycles from 8.2% to 14.2%” Republic of Peru Lima Traffic Management and Sustainable Transport MPA, Item 63 “Average distance 4.5 km reduction 843 tons of CO2 617128 cars, therefore 843/(617128*4.5) ton/km” Republic of Peru Lima Traffic Management and Sustainable Transport MPA, Item 51 The Compact City Scenario – Electrified. University of California, Davis. (2022). UCDAVIS Active Transportation Resource Center Tool. https://activetravelbenefits.ucdavis.edu/ University of California, Davis. (2022). 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Image Credits Page No. Source 7 ITDP 18 ITDP 45 ITDP 48 ITDP 55 ITDP 57 ITDP 58 AdobeStock 63 AdobeStock The Case for Cycling Infrastructure Investments 64 https://cyclingmax.worldbank.org