Publication:
Detecting Urban Clues for Road Safety: Leveraging Big Data and Machine Learning

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Date
2021-11-30
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2021-11-30
Author(s)
Antos, Sarah Elizabeth
Triveno Chan Jan, Luis Miguel
Ghesquiere, Francis
Czapski, Radoslaw
Syed Shafat Ali, Bushra
Gosling-Goldsmith, Jessica
Wang, Charles
Abstract
Transportation services and infrastructure connect people, businesses, and places. They allow citizens to access opportunities, such as jobs, education, health services, recreation, and enable the movement and distribution of goods. As a result, transport services and infrastructure are key to the economic development of cities and regions. The purpose of this guidance note is to provide concrete guidance on how big data and machine learning (ML) can be leveraged in road safety analysis. The document presents opportunities to use these new technologies to improve current methods for data collection and analysis for various road safety assessments. This guidance note provides a practical guide for using new data sources and analytical methods for road safety analysis in different types of projects that may impact road infrastructure or risk-related factors. This document consists of three parts. Part 1 provides an overview of existing approaches and tools for road safety assessment and identifies opportunities to improve these using new technologies such as big data and ML. Part 2 provides an overview of these new technologies and concrete guidance on how they can be integrated into transport projects for road safety analysis. Part 3 presents case studies on two regions of interest – Bogotá, Colombia and Padang, Indonesia to demonstrate how ML can be implemented to evaluate road safety. The document concludes with recommendations for using big data and ML in road safety assessments in the future.
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Antos, Sarah Elizabeth; Triveno Chan Jan, Luis Miguel; Ghesquiere, Francis; Czapski, Radoslaw; Syed Shafat Ali, Bushra; Anapolsky, Sebastian; Gosling-Goldsmith, Jessica; Wang, Charles. 2021. Detecting Urban Clues for Road Safety: Leveraging Big Data and Machine Learning. © Washington, DC: World Bank. http://hdl.handle.net/10986/37029 License: CC BY 3.0 IGO.
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