使用随机参数逻辑模型和整体机器学习方法调查动物与车辆碰撞中影响死亡和受伤的因素
Md Nabil Zawad1, Mohammed Almannaa1, Khalid F Alkahtani1
1Department of Civil Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia.
PloS one
|September 2, 2025
概括
动物与车辆碰撞 (AVC) 造成严重伤害和死亡,特别是在高速公路和维护不良的道路上. 道路设计,围,速度阻碍,天气和动物的存在显著影响了事故的严重性,需要有针对性的安全干预措施.
科学领域:
- 运输安全
- 交通工程
- 道路安全分析
背景情况:
- 动物与车辆相撞 (AVC) 存在重大风险,特别是在农村地区,导致严重伤害和死亡.
- 尽管AVC很少发生, 但它会给大动物带来毁灭性的后果,
研究的目的:
- 调查动物碰撞造成的死亡和受伤的关键因素.
- 分析2016年至2021年间在城市间和主要城市内道路上的1403起动物车辆碰撞观察结果中的24个变量.
主要方法:
- 使用随机参数逻辑模型 (RPLM) 来解释未观察到的异质性并识别显著变量.
- 采用集体机器学习方法和沙普利增量分析 (SHAP) 来全面分析导致事故的因素.
主要成果:
- 高速公路,每方向车道有限,水平曲,路面不良增加严重事故的风险.
- 侧面围的缺失或损坏以及速度阻塞的存在与撞车严重程度的增加有关.
- 驼暴露,假期和恶劣天气对严重事故产生积极影响,而重型卡车的参与则显示出负面联系.
结论:
- 道路设计要素 (高速公路,曲率,路面质量) 和基础设施 (围,阻塞) 在缓解严重的AVC方面至关重要.
- 环境和时间因素,如天气和假期,与动物的存在,显著影响事故的结果.
- 这些发现为政策制定者和道路安全当局提供了可操作的见解,以制定针对严重动物与车辆碰撞的有针对性的对策.
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