Article ; Online: Understanding the Effect of Traffic Congestion on Accidents Using Big Data
Sustainability, Vol 13, Iss 7500, p
2021 Volume 7500
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 ... ...
| 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. |
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| Keywords | traffic accidents ; congestion ; big data ; Latin America ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350 |
| Subject code | 380 |
| Language | English |
| Publishing date | 2021-07-01T00:00:00Z |
| Publisher | MDPI AG |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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