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Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera

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  • Chuanyun Fu
  • Hua Liu

Abstract

To effectively reduce traffic violations that often cause severe crashes at signalized intersections, exploring their contributing factors seems hugely urgent and essential. This study attempted to investigate the influence factors of wrong-way driving (WWD), red-light-running (RLR), violating traffic markings (VTM), and driving in the inaccurate oriented lane (DIOL) at signalized intersections by using data collected from traffic enforcement camera in Hohhot, China. To this end, an ordinary multinomial logit model was developed. By considering the unobserved heterogeneity between observations, a random effects multinomial logit model was proposed as well. After that, the marginal effects of explanatory variables were computed. The outcomes showed that non-local vehicles were more likely to commit WWD and VTM than local vehicles. WWD and RLR frequently occurred in the daytime and evening (6:00–23:59), and on most days within a week. RLR and DIOL mainly happened in June and July. The left-turn lane ratio significantly increased RLR and DIOL. The cloudy, partly cloudy, and rainy days obviously increased WWD and VTM. The temperature from 21 to 30 degrees centigrade was apparently associated with the higher likelihoods of RLR and DIOL. According to the findings of this study, some intervention measures, targeting different vehicle types and considering temporal factors, road, and weather conditions, were recommended to reduce WWD, RLR, VTM, and DIOL at signalized intersections.

Suggested Citation

  • Chuanyun Fu & Hua Liu, 2020. "Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0229653
    DOI: 10.1371/journal.pone.0229653
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    References listed on IDEAS

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    1. Yanyong Guo & Yao Wu & Jian Lu & Jibiao Zhou, 2019. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
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    1. Damian Iwanowicz & Tomasz Krukowicz & Justyna Chadała & Michał Grabowski & Maciej Woźniak, 2024. "Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland," Sustainability, MDPI, vol. 16(20), pages 1-24, October.

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