哪种方法更好地样本极端的交通冲突? 基于传统与机器学习的采样方法
Maryam Hasanpour1, Zhankun Chen2, Carmelo D'Agostino2
1Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.
Accident; analysis and prevention
|February 3, 2026
概括
机器学习方法通过更好地识别极端行人与车辆冲突来提高交通安全. 与传统技术相比,这种方法可以提高撞车风险估计.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 机器学习应用 机器学习应用
背景情况:
- 极端价值理论用于通过采样极端交通冲突来估计碰撞风险.
- 目前的方法缺乏标准化程序,用于值选择和严重程度调整评估.
研究的目的:
- 为了解决传统极端价值理论对碰撞风险采样的局限性.
- 评估机器学习模型,以改善极端交通冲突的采样.
- 为了比较基于机器学习的采样与常规方法的严重程度对齐.
主要方法:
- 研究了自编码神经网络和隔离森林机器学习模型.
- 利用城市信号交叉路口的车辆与行人冲突数据库.
- 机器学习采样与传统基线技术进行比较.
主要成果:
- 机器学习方法产生了极端冲突,这些冲突更好地与概念严重程度水平保持一致.
- 隔离森林证明了经验尾部分布特征的优越保存.
- 基于机器学习的采样为极端价值分布建模提供了改进的上下文表示.
结论:
- 机器学习采样方法,特别是隔离森林,提高了极端交通冲突识别的准确性.
- 与传统方法相比,这些先进的技术为事故风险评估提供了更强大的方法.
- 这些发现支持将机器学习整合到交通安全分析中,以进行积极的风险管理.
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