风险表:探索多种类型的异常驾驶事件和碰撞发生之间的时间关联
Rongjie Yu1, Yang He2, Hao Li2
1College of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804, Shanghai, China.
Accident; analysis and prevention
|July 4, 2024
概括
分析时间事件模式揭示了商业司机的动态撞车风险. 这项研究引入了一种新型变压器模型,通过了解多个异常事件如何影响随时间推移的撞车概率来更好地预测和管理驾驶安全.
科学领域:
- 运输安全运输安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 商业运输企业使用驾驶行为监测进行事故风险评估和干预.
- 目前的方法依赖于即时事件崩关联,无法捕捉时间变化的崩风险趋势.
- 现有的模型缺乏分析多种类型异常驾驶事件对碰撞风险的时间合影响的能力.
研究的目的:
- 探索多种类型的异常驾驶事件和碰撞发生之间的时间关联.
- 开发一个模型,准确地描绘崩风险的时间变化的趋势.
- 改进商业运输领域的积极干预措施.
主要方法:
- 提出了一种对比式学习方法,分析单一事件对碰撞风险的时间影响,整合领域知识和经验数据.
- 使用统一的编码方法和自我注意机制开发了一种新的崩风险评估变压器 (RiskFormer).
- 来自在线乘车服务的实证数据被用于模型培训和评估.
主要成果:
- 确定了三个不同的时间变化的崩风险模式:衰变,增长和日益衰变.
- 与传统模型相比,RiskFormer在曲线下面积 (AUC) 得分上表现出12.8%的改善.
- 该模型有效地捕捉了多类型事件对碰撞风险的时间合影响.
结论:
- 开发的RiskFormer模型通过分析多类型事件的时间合影响,准确地描绘了时间变化的崩风险趋势.
- 这些发现为商业运输的碰撞风险评估提供了重大进展.
- 该研究强调了先进机器学习对提高道路安全和为主动干预提供信息的实际实用性.
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