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To assess traffic risk at intersections during snowy weather, this study introduces a collision risk determination method based on the extreme values of Post-Encroachment Time (PET). Subsequently, the SUMO software is utilized to simulate traffic flow during peak hours under continuous snowfall conditions at the intersection, and the Markov Chain Monte Carlo (MCMC) method is employed to fit the parameters of the extreme value theory model. The results indicate that the MCMC method performs better in handling parameter estimation for the generalized extreme value (GEV) model, and the risk of traffic conflict events in snowy environments is significantly higher than in clear weather.

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Traffic Conflicts-Based Crash Risk Assessment at Intersections Using Extreme Value Theory Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Chuanyun Fu,
  • Jiaming Liu,
  • Ayinigeer Wumaierjiang,
  • Huahua Liu,
  • Zhaoyou Lu,
  • Wei Bai

摘要

To assess traffic risk at intersections during snowy weather, this study introduces a collision risk determination method based on the extreme values of Post-Encroachment Time (PET). Subsequently, the SUMO software is utilized to simulate traffic flow during peak hours under continuous snowfall conditions at the intersection, and the Markov Chain Monte Carlo (MCMC) method is employed to fit the parameters of the extreme value theory model. The results indicate that the MCMC method performs better in handling parameter estimation for the generalized extreme value (GEV) model, and the risk of traffic conflict events in snowy environments is significantly higher than in clear weather.