Big Data in Transportation
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.
| Main Authors: | , |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020-06
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| Subjects: | TRAFFIC CONGESTION, BAYESIAN STRUCTURAL TIME SERIES, COVID-19, CORONAVIRUS, TRANSPORT ANALYSIS, MOBILITY, PANDEMIC IMPACT, BIG DATA, |
| Online Access: | http://documents.worldbank.org/curated/en/144551593524811620/Big-Data-in-Transportation-An-Economics-Perspective https://hdl.handle.net/10986/34023 |
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| Summary: | 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. |
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