谁可能会在高速行驶时遇到硬制动? 对使用动脉道路的低频出租车轨迹和可解释的AI进行常规速度分析
Yue Zhou1, Chuanyun Fu2, Xinguo Jiang3
1Flight Technology College, Civil Aviation Flight University of China, Guanghan 618307, China.
Accident; analysis and prevention
|November 18, 2023
概括
这项研究通过分析超速行为和硬制动事件 (SHEs) 来识别有风险的驾驶员. 机器学习模型,特别是Mini-AlexNet CNN,准确地检测出有高风险超速倾向的驾驶员,从而实现有针对性的安全干预.
科学领域:
- 交通安全 交通安全
- 机器学习应用 机器学习应用
- 行为分析 行为分析
背景情况:
- 常规的超速者表现出复发性,某些超速行为会造成更大的道路危险.
- 评估与不同速度倾向相关的驾驶风险对于有针对性的安全措施至关重要.
研究的目的:
- 识别和分析风险的超速行为,使用与超速相关的硬制动事件 (SHEs) 作为安全替代品.
- 根据他们的超速倾向,开发和比较机器学习模型来识别遇到SHE的常规速度者.
主要方法:
- 从GPS轨迹中确定了超速行为和硬制动事件,通过将速度与发布的限制进行比较并分析速度曲线.
- 为了分类,开发了物流回归,XGBoost和三个1D卷积神经网络 (CNN).
- 为了模型的可解释性,使用了沙普利增量扩展 (SHAP) 框架.
主要成果:
- 迷你亚历克斯网CNN模型实现了卓越的性能,F1得分为91%,回忆率为90%.
- 在短距离 (≤285m) 上,超过50%的速度被确定为最危险的行为.
- 特定的道路状况,如缺乏自行车道或路边停车场的存在,增加了SHE的可能性.
结论:
- 量身定制的建议可以有效地限制与危险相关的超速行为,而不是所有超速行为.
- 机器学习模型,特别是CNN,显示出从GPS数据中识别高风险驾驶员的巨大潜力.
- 了解超速的背景对于制定有效的道路安全战略至关重要.
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