Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning
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...
| Main Authors: | , , , , , , , |
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| Format: | Report |
| Language: | English |
| Published: |
Washington, DC: World Bank
2022
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| Online Access: | http://documents.worldbank.org/curated/en/099200002152227482/P170812026cd2b0550acec0ef8165301833 http://hdl.handle.net/10986/37029 |
| Summary: | 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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