@inproceedings{16ceb58b94e14239b74bd931adb4bfa4,
title = "Robustness Evaluation of Emerging Mixed Traffic Flow in Snowy Weather Using Extreme Value Theory",
abstract = "To evaluate the robustness of emerging mixed traffic flow under snowy conditions from the perspective of crash risk, this study proposes a TTC (Time-to-Collision) dynamic threshold determination method based on extreme value theory to quantify the crash risk. This study simulates traffic flow during peak hours under continuous snowfall conditions in the SUMO software, and use eta-squared to evaluate the robustness of traffic flow under the various Market Penetration Rates (MPR). The results indicate that at different extreme quantiles, as MPR increases, eta-squared shows a downward trend. Especially under high MPR (80\%), eta-squared can decrease by more than 45\%. This result indicates that emerging mixed traffic flow under high MPRs can effectively improve the robustness under snowy conditions and reduce crash risk compared to traditional traffic flow.",
keywords = "Connected and automated vehicle, Crash risk, Extreme value theory, Snow weather condition, Traffic conflict, Traffic safety",
author = "Chuanyun Fu and Huahua Liu and Zhaoyou Lu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 7th KES International Symposium on Smart Transport Systems, KES-STS 2024 ; Conference date: 19-06-2024 Through 21-06-2024",
year = "2024",
doi = "10.1007/978-981-97-6748-9\_14",
language = "英语",
isbn = "9789819767472",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "155--165",
editor = "Kun Gao and Yiming Bie and R.J. Howlett and Jain, \{Lakhmi C.\}",
booktitle = "Smart Transportation Systems 2024 - Proceedings of 7th KES-STS International Symposium",
address = "德国",
}