Big Data in Transportation : An Economics Perspective
This paper reviews the emerging big data literature applied to urban transportation issues from the perspective of economic research. It provides a typology of big data sources relevant to transportation analyses and describes how these data can be...
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Online Access: | http://documents.worldbank.org/curated/en/144551593524811620/Big-Data-in-Transportation-An-Economics-Perspective http://hdl.handle.net/10986/34023 |
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okr-10986-340232022-09-20T00:11:28Z Big Data in Transportation : An Economics Perspective Selod, Harris Soumahoro, Souleymane TRAFFIC CONGESTION BAYESIAN STRUCTURAL TIME SERIES COVID-19 CORONAVIRUS TRANSPORT ANALYSIS MOBILITY PANDEMIC IMPACT BIG DATA This paper reviews the emerging big data literature applied to urban transportation issues from the perspective of economic research. It provides a typology of big data sources relevant to transportation analyses and describes how these data can be used to measure mobility, associated externalities, and welfare impacts. As an application, it showcases the use of daily traffic conditions data in various developed and developing country cities to estimate the causal impact of stay-at-home orders during the Covid-19 pandemic on traffic congestion in Bogotá, New Dehli, New York, and Paris. In light of the advances in big data analytics, the paper concludes with a discussion on policy opportunities and challenges. 2020-07-06T15:10:07Z 2020-07-06T15:10:07Z 2020-06 Working Paper http://documents.worldbank.org/curated/en/144551593524811620/Big-Data-in-Transportation-An-Economics-Perspective http://hdl.handle.net/10986/34023 English Policy Research Working Paper;No. 9308 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Colombia France India United States |
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Digital Repository |
institution_category |
Foreign Institution |
institution |
Digital Repositories |
building |
World Bank Open Knowledge Repository |
collection |
World Bank |
language |
English |
topic |
TRAFFIC CONGESTION BAYESIAN STRUCTURAL TIME SERIES COVID-19 CORONAVIRUS TRANSPORT ANALYSIS MOBILITY PANDEMIC IMPACT BIG DATA |
spellingShingle |
TRAFFIC CONGESTION BAYESIAN STRUCTURAL TIME SERIES COVID-19 CORONAVIRUS TRANSPORT ANALYSIS MOBILITY PANDEMIC IMPACT BIG DATA Selod, Harris Soumahoro, Souleymane Big Data in Transportation : An Economics Perspective |
geographic_facet |
Colombia France India United States |
relation |
Policy Research Working Paper;No. 9308 |
description |
This paper reviews the emerging big data
literature applied to urban transportation issues from the
perspective of economic research. It provides a typology of
big data sources relevant to transportation analyses and
describes how these data can be used to measure mobility,
associated externalities, and welfare impacts. As an
application, it showcases the use of daily traffic
conditions data in various developed and developing country
cities to estimate the causal impact of stay-at-home orders
during the Covid-19 pandemic on traffic congestion in
Bogotá, New Dehli, New York, and Paris. In light of the
advances in big data analytics, the paper concludes with a
discussion on policy opportunities and challenges. |
format |
Working Paper |
author |
Selod, Harris Soumahoro, Souleymane |
author_facet |
Selod, Harris Soumahoro, Souleymane |
author_sort |
Selod, Harris |
title |
Big Data in Transportation : An Economics Perspective |
title_short |
Big Data in Transportation : An Economics Perspective |
title_full |
Big Data in Transportation : An Economics Perspective |
title_fullStr |
Big Data in Transportation : An Economics Perspective |
title_full_unstemmed |
Big Data in Transportation : An Economics Perspective |
title_sort |
big data in transportation : an economics perspective |
publisher |
World Bank, Washington, DC |
publishDate |
2020 |
url |
http://documents.worldbank.org/curated/en/144551593524811620/Big-Data-in-Transportation-An-Economics-Perspective http://hdl.handle.net/10986/34023 |
_version_ |
1764480018888523776 |