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通过通用帕雷托回归树来分析恶劣天气中极端的多车辆后部碰撞风险
Li Li1, Jing-Yu Yang1, Meng Zhang2,3
1Department of Traffic Information and Control Engineering, School of Electronics and Control Engineering, Chang'an University, Xi'an, China.
Traffic injury prevention
|July 16, 2025
概括
本研究引入了一个框架,以评估在恶劣天气下后方碰撞的风险. 它确定了速度,前进和道路状况等关键因素,这些因素对于提高汽车跟踪安全至关重要.
科学领域:
- 交通安全 交通安全
- 风险评估 风险评估
- 极端天气驾驶 极端天气驾驶
背景情况:
- 后端碰撞在多车辆交通中构成重大风险.
- 恶劣的天气条件加剧了这些碰撞风险.
- 现有的方法可能缺乏细节性,无法识别特定的风险因素.
研究的目的:
- 开发和应用一个综合框架,用于评估在多车辆汽车追随的场景后端碰撞的风险.
- 在各种天气条件下识别关键风险因素及其影响值.
- 加强汽车跟踪情况的安全评估工具.
主要方法:
- 利用扩展的概率驾驶风险场来评估碰撞风险.
- 应用通用帕雷托分布用于定量风险分析和高风险场景选.
- 采用通用帕雷托回归树 (GPRT) 来识别导致风险的因素及其值,整合极端价值理论.
主要成果:
- 发现碰撞风险很好地符合一般化的帕雷托分布.
- 在准确性和复杂性方面,GPRT方法的表现优于通用增量模型 (GAM) 和负二项式回归 (NBR).
- 车辆的速度,重量和前进是关键因素,前进和道路表面条件在恶劣天气中变得越来越有影响.
结论:
- 开发的综合框架有效地评估了极端天气下汽车跟踪的安全性.
- GPRT模型为风险分析提供了卓越的解释性和工程应用性.
- 研究结果强调了随着天气条件的变化,各种因素对碰撞风险的动态影响.
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