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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Jun (editor) / Li, Wang (editor) / Geng, Xiongfei (editor) / Zhang, Ke (editor) / Ji, Honghai (editor) / Li, Kailong (editor) / Fu, Chuanyun (author) / Liu, Jiaming (author) / Wumaierjiang, Ayinigeer (author) / Liu, Huahua (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-04-02


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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