调查影响重复撞车驾驶员 (RCID) 的因素:一种基于危险持续时间的随机参数方法
Hala A Eljailany1, Jaeyoung Jay Lee1, Helai Huang1
1School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
Accident; analysis and prevention
|December 9, 2024
概括
经常发生碰撞的司机 (RCID) 面临着由驾驶史和未观察到的因素影响的不断变化的风险. 通过解决这些复杂的影响,有针对性的干预对于改善交通安全至关重要.
科学领域:
- 交通安全研究 交通安全研究
- 驾驶员行为分析分析
- 风险建模风险建模
背景情况:
- 经常发生碰撞的司机 (RCID) 不成比例地导致交通事故和严重后果.
- 现有的研究往往忽视了碰撞史和碰撞间隔对演变的驾驶员风险的影响.
- 传统模型很难解释在重复碰撞中未被观察到的异质性.
研究的目的:
- 调查影响重复发生事故的司机 (RCIDs) 的多方面的因素.
- 发展对驾驶员行为动态和碰撞风险的全面理解.
- 整合机器学习与先进的统计建模进行增强分析.
主要方法:
- 混合方法结合机器学习 (ML) 进行因子识别.
- 随机参数基于危险的持续时间模型 (HBDM) 来解决未观察到的异质性.
- 将ML识别的关键因素纳入HBDM框架.
主要成果:
- 男司机,分心/酒精驾驶史,以及先前的违法行为增加了碰撞的风险.
- 道路状况,车辆年龄和地区因素对事故发生有很大影响.
- 拥有大量事故历史的司机显示出动态的风险概况和随着时间的推移增加了事故发生的可能性.
结论:
- 未观察到的异质性 (Theta) 突出了潜在的,驾驶员特定的风险因素,特别是在高风险驾驶员中.
- 研究结果揭示了可观测和未测量的影响对重复撞车参与的复杂相互作用.
- 强调需要针对性的交通安全干预措施,以解决细微的驾驶员行为.
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