@inproceedings{dabb012adaa846398c733290378025c9,
title = "Traffic Conflicts-Based Crash Risk Assessment at Tunnels Using Extreme Value Theory Approach",
abstract = "Tunnels are bottlenecks in highway traffic, characterized by a closed driving environment. Due to the unique nature of the traffic environment within tunnels, traffic safety accidents occur frequently. However, there are still certain limitations in the current research on tunnel safety evaluation. This paper utilizes the advanced object detection technology YOLOv8 to extract traffic state parameters of vehicles from tunnel surveillance videos, and employs an extreme value theory model based on Time to Collision (TTC) to assess the collision risks of different lanes within the tunnel. Experimental results indicate that the collision risk is lowest in the slow lane. Although the conflict rates for the fast lane and the middle lane are similar, the collision risk in the fast lane is greater than that in the middle lane.",
keywords = "Extreme Value Theory, Time to Collision, Traffic Conflict, Tunnels, YOLOv8",
author = "Chuanyun Fu and Han Yan and Zhaoyou Lu and Jiaming Liu and Huahua Liu and Wei Bai",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 8th KES International Symposium on Smart Transport Systems, KES-STS 2025 ; Conference date: 25-06-2025 Through 27-06-2025",
year = "2026",
doi = "10.1007/978-3-032-20963-4\_19",
language = "英语",
isbn = "9783032209627",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "187--197",
editor = "Kun Gao and Yang Liu and Chuanyun Fu and Robert Howlett and Jain, \{Lakshmi. C\}",
booktitle = "Smart Transportation Systems 2025 - Proceedings of 8th KES-STS International Symposium",
address = "德国",
}