使用大规模几何特征进行网络级崩风险分析
Shi Qiu1, Hanzhang Ge2, Zheng Li2
1School of Civil Engineering, Central South University, Changsha 410075, China; MOE Key Laboratory of Engineering Structures of Heavy-haul Railway, Changsha 410075, China; Intelligent Monitoring Research Center of Rail Transit Infrastructure, Changsha 410075, China.
Accident; analysis and prevention
|August 17, 2024
概括
本研究引入了一种使用开源数据和先进机器学习识别道路事故风险的新方法. HHO-XGBoost模型有效地分析了道路几何形状,以改善交通安全预测.
科学领域:
- 运输工程 运输工程
- 道路交通安全 道路交通安全
- 机器学习应用 机器学习应用
背景情况:
- 道路交通事故带来了重大的社会风险和经济损失.
- 鉴定事故的原因是复杂的,因为交互因素,如司机,车辆,道路和环境.
- 大规模崩预测在数据收集和成本方面面临挑战.
研究的目的:
- 利用开源数据开发一种具有成本效益的方法来识别大规模道路网络事故风险.
- 为了利用道路几何形状,特别是水平曲线 (H曲线) 和垂直曲线 (V曲线),进行碰撞风险评估.
- 引入一个优化的机器学习模型,用于增强碰撞预测.
主要方法:
- 从水平和垂直的道路曲线中提取特征.
- 哈里斯·霍克斯优化 (HHO) 算法的开发与XGBoost模型 (HHO-XGBoost) 的结合.
- 为模型培训和验证创建专门的道路几何碰撞风险数据集.
主要成果:
- 在HHO-XGBoost模型的适应性确定了最佳的XGBoost超参数.
- 在使用开发的模型预测碰撞风险方面取得了有利的结果.
- 对于大型道路网络,成功完成了"区域-道路-部分"的分层风险分析.
结论:
- 拟议的方法为大规模道路网络的撞车风险识别提供了一种可行的方法.
- 该HHO-XGBoost模型在分析道路几何学上安全性方面表现出有效性.
- 该研究提供了一个3D道路几何数据库,并对集成学习模型的群体智能提供了洞察力.
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