通过使用统计和数据驱动模型,分析速度差对前后车辆在高速公路关节损伤严重性的影响
Chenzhu Wang1, Mohamed Abdel-Aty1, Lei Han1
1Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.
Accident; analysis and prevention
|July 7, 2024
概括
这项研究发现,交叉合多层感知子 (CS-MLP) 网络最好地预测后端 (RE) 碰撞伤害严重程度. 当车辆之间的速度差异 (Δν) 为0-10英里/小时时时,安全性是最佳的.
科学领域:
- 交通安全 交通安全 交通安全
- 事故分析 事故分析
- 机器学习在运输中的应用
背景情况:
- 后端 (RE) 撞车在高速公路上是一个重大的安全问题,受伤严重程度受到各种因素的影响.
- 了解速度差 (Δν) 和RE碰撞伤害严重程度之间的关系对于制定有效的缓解策略至关重要.
研究的目的:
- 为了比较统计模型和数据驱动方法对RE碰撞伤害严重性的预测性能.
- 为了研究两辆车的RE碰撞中速度差 (Δν) 和受伤严重程度之间的相关性.
- 确定影响后行和前行车辆受伤严重程度的关键变量.
主要方法:
- 在两年 (2021-2022) 中利用了来自15980起两辆车的RE车祸的数据.
- 将随机参数双变探针 (RPBP) 模型与数据驱动模型进行比较:支持矢量机 (SVM),极端梯度增强 (XGBoost) 和多层感知器 (MLP) 网络.
- 采用跨多层感知子 (CS-MLP) 网络进行先进的预测分析.
主要成果:
- 与所有其他模型相比,CS-MLP网络显示出优异的预测性能 (回忆,F-1分数,AUC).
- 确定了影响两辆车损伤严重程度的共享变量,并注意到车辆类型 (例如,卡车与乘用车) 的对比影响.
- 沙普利增材扩张 (SHAP) 揭示了速度差异 (Δν) 和受伤严重程度之间的非线性关系,受伤风险在0-10英里/小时的Δν处最低.
结论:
- 联合建模方法,特别是CS-MLP网络,可以更好地预测RE碰撞伤害严重程度.
- 速度差 (Δν) 对伤害结果有非线性影响,对最小化伤害的危险范围具有关键范围.
- 建议采取动态速度控制措施,以减少两辆车的RE碰撞中受伤的严重程度.
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