一个空间结构化的实证贝叶斯框架,用于评估整个网络的安全对策
Mingjian Wu1, Aurélie Labbe2, Alexandra M Schmidt3
1Department of Civil Engineering, McGill University, Montréal, QC H3A 0C3, Canada.
Accident; analysis and prevention
|February 6, 2026
概括
本研究引入了一个新的实证贝叶斯框架,使用网络流程卷积来进行道路安全分析. 它改善了网络范围内的对策的不确定性量化,有助于运输安全管理.
科学领域:
- 运输工程 运输工程
- 统计建模 统计建模
- 道路安全分析 道路安全分析
背景情况:
- 由于局部和溢出效应,评估道路安全对策具有挑战性.
- 准确的不确定性量化对于有效的安全管理至关重要.
研究的目的:
- 提出一个两步,空间结构化的实证湾 (EB) 框架,用于评估整个网络的道路安全对策.
- 利用网络流程卷积 (NPC) 模型进行改进的估计和不确定性量化.
主要方法:
- 开发了一个两步EB框架,将NPC模型与基于网络的高斯过程结合起来.
- 利用重量化的内核卷积来捕捉道路网络上的碰撞的空间相关性.
- 进行模拟研究,并将该方法应用于现实世界的驾驶员反信号程序.
主要成果:
- 比传统的EB-PG,EB-NPC框架提供了更可靠,更精准的不确定性量化,特别是与空间相关性.
- 这两种方法都能准确地回收反事实碰撞,但EB-NPC提供了更具信息性的预测不确定性.
- EB-NPC生成连续的空间风险表面,用于可视化和优先考虑高风险细分市场.
结论:
- 拟议的EB-NPC框架加强了反事实结果的恢复,并提供了准确的,可解释的不确定性特征.
- 这种方法为数据驱动的运输安全管理提供了一个强大的工具.
- EB-NPC有助于可视化整个网络的安全模式,并优先考虑干预措施.
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