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Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects (Inglês)

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. While the development of transportation systems and infrastructure is vital to economic growth, it is also important to evaluate and mitigate its potential negative externalities and costs to society. 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 road safety assessment procedures across the project cycle, in accordance with the World Bank’s latest Environmental and Social Framework (ESF) guidelines. This guidance note is for World Bank task teams who are interested in using new data sources and analytical methods for road safety analysis across various types of projects. This document consists of three parts. Part 1 discusses the World Bank’s current guidelines for incorporating road safety analysis across the project cycle, examines existing data and approaches and identifies opportunities to improve current methods using big data and ML. Part 2 provides an overview of these new technologies and concrete guidance on how they can be integrated into World Bank projects. 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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