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A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways

Author

Listed:
  • Zhaoshi Geng

    (School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China)

  • Xiaofeng Ji

    (School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China)

  • Rui Cao

    (School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China)

  • Mengyuan Lu

    (School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China)

  • Wenwen Qin

    (School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    Yunnan Engineering Research Center of Modern Logistics, Kunming University of Science and Technology, Kunming 650504, China)

Abstract

Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, machine learning algorithms were used to identify critical feature parameters that affect truck collision risk. Eventually, extreme value theory based on time to collision and post-encroachment time incorporated a machine learning algorithm to identify high-risk truck driving scenes. The experiments showed that bivariate extreme value theory integrates the applicability of time to collision and post-encroachment time for different driving trajectories of trucks, resulting in significantly better prediction performances than univariate extreme value theory. Additionally, the horizontal curve radius has the most critical impact on truck collision; when a truck is driving on two-lane rural highways with a horizontal curve radius of 227 m or less, the frequency and probability of collision will be higher, and deceleration devices and central guardrail barriers can be installed to reduce risk. Second is the driving behavior risk: the driving behavior of truck drivers on two-lane rural highways has high-risk, and we recommend the installation of speed cameras on two-lane rural roads to control the driving speed of trucks and thus avoid dangerous driving behaviors. This study extends the evaluation method of truck collisions on two-lane rural highways from univariate to bivariate and provides a basis for the design of two-lane rural highways and the development of real-time dynamic warning systems and enforcement for trucks, which will help prevent and control truck collisions and alleviate safety problems on two-lane rural highways.

Suggested Citation

  • Zhaoshi Geng & Xiaofeng Ji & Rui Cao & Mengyuan Lu & Wenwen Qin, 2022. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11212-:d:909102
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    References listed on IDEAS

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    1. Jiajun Shen & Guangchuan Yang, 2020. "Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data," Sustainability, MDPI, vol. 12(23), pages 1-18, November.
    2. Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2022. "An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. Miaomiao Liu & Yongsheng Chen, 2017. "Predicting Real-Time Crash Risk for Urban Expressways in China," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, January.
    4. Adham Alsharkawi & Mohammad Al-Fetyani & Maha Dawas & Heba Saadeh & Musa Alyaman, 2021. "Poverty Classification Using Machine Learning: The Case of Jordan," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    5. Jinliang Xu & Tian Xin & Chao Gao & Zhenhua Sun, 2022. "Study on the Maximum Safe Instantaneous Input of the Steering Wheel against Rollover for Trucks on Horizontal Curves," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    6. Yingshuai Li & Jian Lu & Kuisheng Xu, 2017. "Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-12, August.
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