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Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining

Author

Listed:
  • Fu Wang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Jing Wang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Xianfeng Zhang

    (Wuhan Transportation Planning & Design Co., Ltd., Wuhan 430010, China)

  • Dengjun Gu

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Yang Yang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Hongbin Zhu

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

Abstract

China has a large vehicle base, uneven road conditions, and the highest rate of traffic accidents in the world. Particularly on the long downhill sections of expressway tunnels in mountainous areas with harsh geographical conditions, traffic accidents are densely distributed, and once a traffic accident occurs, the consequences are serious, which poses a large threat to people’s lives and property. This paper mined and analyzed the traffic accident data collected by the project on the Baoding section of Zhangshi Expressway. SPSS software was used to analyze the traffic accident data characteristics of the long downhill tunnel of the mountain expressways. The time, space, accident form, vehicle type, and road alignment distribution characteristics of the traffic accident in the long downhill tunnel section of mountain expressways were obtained. The decision tree algorithm was used to construct the cause analysis model of traffic accidents in the long downhill tunnel of mountain expressways, and the five primary influencing factors were obtained: horizontal curve radius, week, slope length, time, and cart ratio. The improved cumulative frequency curve method was used to study the accident-prone points of mountain expressways, and the accident-prone points and potential accident-prone points were obtained.

Suggested Citation

  • Fu Wang & Jing Wang & Xianfeng Zhang & Dengjun Gu & Yang Yang & Hongbin Zhu, 2022. "Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8460-:d:859938
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    References listed on IDEAS

    as
    1. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    2. Yajie Zou & Yue Zhang & Kai Cheng, 2021. "Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents," Sustainability, MDPI, vol. 13(1), pages 1-14, January.
    3. Jianfeng Xi & Zhenhai Gao & Shifeng Niu & Tongqiang Ding & Guobao Ning, 2013. "A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, February.
    4. Zihao Wen & Hui Zhang & Ronghui Zhang, 2021. "Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
    5. Yuzhou Tang & Xiaodang Peng & Shiyong Xu & Mingju Bai & Lifang Lin & Haihan Sun & Naeem Jan, 2022. "Study on Driver Gaze Characteristics in Sight Distance Limited Section of Mountain Highway Based on Visual Information," Journal of Mathematics, Hindawi, vol. 2022, pages 1-8, January.
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    Cited by:

    1. Agnieszka Bekisz & Michal Kruszynski, 2022. "Using the Methodology of Network Thinking to Solve a Problem Situation on the Example of Road Transport," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 30-45.
    2. Xiangyu Wei & Shixiang Tian & Zhangyin Dai & Peng Li, 2022. "Statistical Analysis of Major and Extra Serious Traffic Accidents on Chinese Expressways from 2011 to 2021," Sustainability, MDPI, vol. 14(23), pages 1-18, November.

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