Analyzing extreme muti-vehicle rear-end collision risks in adverse weather through Generalized Pareto Regression Trees
The integrated evaluation framework developed in this study provides a tool for car-following safety assessment under extreme weather conditions.
Authors
- Qing-Chang Lu (ORCID: https://orcid.org/0000-0001-9616-2271)
- Zhu Feng (ORCID: https://orcid.org/0000-0001-5699-9333)
- Li Li (ORCID: https://orcid.org/0000-0002-1963-8587)
- Qiang Niu
- Jingyu Yang (ORCID: https://orcid.org/0000-0001-5187-5817)
- Meng Zhang (ORCID: https://orcid.org/0009-0006-0354-7118)
Institutions
- Nanyang Technological University (SG)
- Chang'an University (CN)
- China Communications Construction Company (China) (CN)
- Yunnan Province Science and Technology Department (CN)
- Yunnan Investment Group (China) (CN)
Publication Details
- Journal
- Traffic Injury Prevention
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1080/15389588.2025.2517386
- Primary Topic
- Traffic and Road Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China