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Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways

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Listed:
  • Wenhui Zhang

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Tuo Liu

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Jing Yi

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention.

Suggested Citation

  • Wenhui Zhang & Tuo Liu & Jing Yi, 2022. "Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11761-:d:918915
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    References listed on IDEAS

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    1. Qingwan Xue & Xuedong Yan & Xiaomeng Li & Yun Wang, 2018. "Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-13, September.
    2. Qiang Luo & Xiaodong Zang & Jie Yuan & Xinqiang Chen & Junheng Yang & Shubo Wu, 2020. "Research of Vehicle Rear-End Collision Model considering Multiple Factors," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, April.
    3. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    4. Ran Wei & Song Chen & Saifei Zhang & Jiaqi Zhang & Rujun Ding & Jiang Mi & Shangce Gao, 2022. "An AHP-ME-Based Vehicle Crash Prediction Model considering Driver Intention and Real-Time Traffic/Road Condition," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, July.
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