@inproceedings{4deee9c53a944f3aa3b439d1489fe4ab,
title = "Traffic Conflicts-Based Crash Risk Assessment at Intersections Using Extreme Value Theory Approach",
abstract = "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.",
keywords = "Crash Risk, Extreme Value Theory, Markov Chain Monte Carlo, Post Encroachment Time, Snow Weather, Traffic Conflict",
author = "Chuanyun Fu and Jiaming Liu and Ayinigeer Wumaierjiang and Huahua Liu and Zhaoyou Lu and Wei Bai",
note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2025.; International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024 ; Conference date: 06-12-2024 Through 08-12-2024",
year = "2025",
doi = "10.1007/978-981-96-3977-9\_26",
language = "英语",
isbn = "9789819639762",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "236--243",
editor = "Jun Liu and Wang Li and Xiongfei Geng and Ke Zhang and Honghai Ji and Kailong Li",
booktitle = "The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume VI",
address = "德国",
}