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

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, transp...

Full description

Bibliographic Details
Main Author:	World Bank
Format:	Report
Language:	English
Published:	Washington, DC: World Bank 2022
Online Access:	http://documents.worldbank.org/curated/en/099200002152228754/P170812020245f053097a50eeba0230ef35 http://hdl.handle.net/10986/37043

id	okr-10986-37043
recordtype	oai_dc
spelling	okr-10986-370432022-02-26T05:10:32Z Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects World Bank 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. 2022-02-25T22:11:15Z 2022-02-25T22:11:15Z 2021-11-30 http://documents.worldbank.org/curated/en/099200002152228754/P170812020245f053097a50eeba0230ef35 http://hdl.handle.net/10986/37043 English CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank Washington, DC: World Bank Report Publications & Research Other Other World
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
geographic_facet	Other Other World
description	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.
format	Report
author	World Bank
spellingShingle	World Bank Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
author_facet	World Bank
author_sort	World Bank
title	Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
title_short	Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
title_full	Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
title_fullStr	Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
title_full_unstemmed	Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning in World Bank Transport Projects
title_sort	detecting urban clues for road safety : leveraging big data and machine learning in world bank transport projects
publisher	Washington, DC: World Bank
publishDate	2022
url	http://documents.worldbank.org/curated/en/099200002152228754/P170812020245f053097a50eeba0230ef35 http://hdl.handle.net/10986/37043
_version_	1764486361499303936

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

Similar Items