推进主动碰撞预测:用于预测碰撞和严重程度的离散持续时间方法
Diwas Thapa1, Sabyasachee Mishra1, Nagendra R Velaga2
1Department of Civil Engineering, University of Memphis, Memphis, TN 38152, United States.
Accident; analysis and prevention
|December 6, 2023
概括
这项研究引入了一个基于持续时间的新框架,用于预测交通事故及其严重程度. 15%的数据样本在碰撞预测建模中提供了准确性和效率之间的平衡.
科学领域:
- 运输工程 运输工程
- 统计建模 统计建模
- 道路交通安全 道路交通安全
背景情况:
- 积极的崩预测模型越来越多地使用机器学习和人工智能.
- 统计模型提供因果洞察和影响大小估计.
- 现有的统计模型经常使用病例控制方法,在比率定义和严重程度纳入方面存在挑战.
研究的目的:
- 扩展基于持续时间的建模框架,用于预测交通事故及其严重程度.
- 在纳入撞击严重程度时,调查模型性能和估计时间之间的权衡.
- 确定最佳的数据采样策略,以进行可靠的崩预测.
主要方法:
- 开发了一个基于持续时间的新框架,整合了事故严重程度.
- 探索数据采样策略 (15%的时代级样本) 以管理计算复杂性.
- 对不同样本大小的预测变量进行稳定性分析.
主要成果:
- 15%的时代级样本在数据大小和预测准确性之间提供了一个平衡的方法.
- 某些变量 (例如,白天的时间,天气,照明,体积) 需要更大的样本来进行稳定的估计.
- 其他变量 (例如,日间,地形,土地使用,车道数量,速度) 与较小的样本增加相聚.
- 该模型在高速公路段上表现更好,碰撞时间较短 (<100小时).
结论:
- 提出的基于持续时间的框架有效地预测了机和严重程度.
- 时代级数据采样,特别是15%,是平衡模型性能和计算效率的可行策略.
- 了解对样本大小的变量灵敏度有助于优化数据收集和道路安全模型开发.
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