使用基于多源传感数据的极端值模型对重型车辆驾驶员的安全评估
Chenxiao Zhang1, Yongfeng Ma2, Tarek Sayed3
1School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Department of Civil Engineering, The University of British Columbia, Canada.
Accident; analysis and prevention
|August 26, 2025
概括
这项研究引入了使用极端价值建模的重型车辆的新安全框架. 它确定了有效预测和管理事故风险的关键驱动指标和门.
科学领域:
- 运输工程
- 数据科学
- 风险管理
背景情况:
- 智能网络平台收集重型车辆的多源传感数据,以确保安全.
- 现有的碰撞风险管理和安全评估方法不能充分利用这些驾驶数据.
研究的目的:
- 为重型车辆提出新的安全评估框架.
- 制定确定崩风险值和分析指标依赖性的方法.
主要方法:
- 采用极端价值建模,包括基于单变量和双变量的物流模型.
- 在3,452个重型车辆行程的数据集中应用了区块最大模型.
- 对于速度变动的随机波动 (速度-Vf) 和冲动的确定的值.
主要成果:
- 速度-Vf和冲动是重型车辆驾驶风险评估的有效指标.
- 没有负载的条件和高分心/疲劳警告增加了碰撞的风险.
- 确定了速度-Vf和Jerk的最佳值以进行撞击估计.
结论:
- 拟议的框架有效评估重型车辆的安全性.
- 双变量模型提供比单变量模型更可靠的撞车风险估计.
- 这项研究强调了分析共同概率对于准确的风险评估的重要性.
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