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.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development
Cited by
15 articles.
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