Understanding the Effect of Traffic Congestion on Accidents Using Big Data

Santiago Sánchez González, Felipe Bedoya-Maya and Agustina Calatayud
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Santiago Sánchez González: Transport Division, Inter-American Development Bank, Washington, DC 20577, USA
Felipe Bedoya-Maya: Transport Division, Inter-American Development Bank, Washington, DC 20577, USA

Sustainability, 2021, vol. 13, issue 13, 1-19

Abstract: Understanding the temporal and spatial dynamics of traffic accidents are a key determinant in their mitigation. This article leverages big data and a Poisson model with fixed effects to understand the causality of traffic congestion on road accidents in ten cities in Latin America: Bogota, Buenos Aires, Lima, Mexico City, Montevideo, Rio de Janeiro, San Salvador, Santiago, Santo Domingo, and Sao Paulo. Analyzing over 10 billion observations in 2019, results show a positive non-linear causality of congestion on the number of accidents. Overall, the results suggest that a 10% reduction in traffic delay would reduce accidents by 3.4%, equivalent to over 72 thousand traffic accidents. Sao Paulo and Mexico City would be particularly benefited, with reductions of 5.4% and 4.7%, respectively. The results of this paper aim to support policymakers in emerging economies in implementing measures to reduce congestion and, with it, the related direct and indirect costs borne by societies.

Keywords: traffic accidents; congestion; big data; Latin America (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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