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Red-Light-Running Crashes’ Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database

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Listed:
  • Yuting Zhang

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xuedong Yan

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaomeng Li

    (Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia)

  • Jiawei Wu

    (Center for Advanced Transportation System Simulation, Department of Civil Environment Construction Engineering, University of Central Florida, Orlando, FL 32801, USA)

  • Vinayak V. Dixit

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Randwick, NSW 2052, Australia)

Abstract

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Suggested Citation

  • Yuting Zhang & Xuedong Yan & Xiaomeng Li & Jiawei Wu & Vinayak V. Dixit, 2018. "Red-Light-Running Crashes’ Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database," IJERPH, MDPI, vol. 15(6), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:6:p:1290-:d:153277
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    References listed on IDEAS

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    1. Baratian-Ghorghi, Fatemeh & Zhou, Huaguo & Zech, Wesley C., 2016. "Red-light running traffic violations: A novel time-based method for determining a fine structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 55-65.
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    Cited by:

    1. Arshad Jamal & Muhammad Tauhidur Rahman & Hassan M. Al-Ahmadi & Umer Mansoor, 2019. "The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies," IJERPH, MDPI, vol. 17(1), pages 1-23, December.

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