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...
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| Format: | Report |
| Language: | English |
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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 |
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okr-10986-370292022-02-25T05:10:38Z Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning Antos, Sarah Elizabeth Triveno Chan Jan, Luis Miguel Ghesquiere, Francis Czapski, Radoslaw Syed Shafat Ali, Bushra Anapolsky, Sebastian Gosling-Goldsmith, Jessica Wang, Charles 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. 2022-02-24T19:51:52Z 2022-02-24T19:51:52Z 2021-11-30 http://documents.worldbank.org/curated/en/099200002152227482/P170812026cd2b0550acec0ef8165301833 http://hdl.handle.net/10986/37029 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. 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. |
| format |
Report |
| author |
Antos, Sarah Elizabeth Triveno Chan Jan, Luis Miguel Ghesquiere, Francis Czapski, Radoslaw Syed Shafat Ali, Bushra Anapolsky, Sebastian Gosling-Goldsmith, Jessica Wang, Charles |
| spellingShingle |
Antos, Sarah Elizabeth Triveno Chan Jan, Luis Miguel Ghesquiere, Francis Czapski, Radoslaw Syed Shafat Ali, Bushra Anapolsky, Sebastian Gosling-Goldsmith, Jessica Wang, Charles Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| author_facet |
Antos, Sarah Elizabeth Triveno Chan Jan, Luis Miguel Ghesquiere, Francis Czapski, Radoslaw Syed Shafat Ali, Bushra Anapolsky, Sebastian Gosling-Goldsmith, Jessica Wang, Charles |
| author_sort |
Antos, Sarah Elizabeth |
| title |
Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| title_short |
Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| title_full |
Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| title_fullStr |
Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| title_full_unstemmed |
Detecting Urban Clues for Road Safety : Leveraging Big Data and Machine Learning |
| title_sort |
detecting urban clues for road safety : leveraging big data and machine learning |
| publisher |
Washington, DC: World Bank |
| publishDate |
2022 |
| url |
http://documents.worldbank.org/curated/en/099200002152227482/P170812026cd2b0550acec0ef8165301833 http://hdl.handle.net/10986/37029 |
| _version_ |
1764486353810096128 |