使用最大稳定过程方法,用于交通冲突的空间通用极端值框架
Quansheng Yue1, Yanyong Guo1, Tarek Sayed2
1School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, Jiangsu Collaborative Innovation Center of Modern, Urban Traffic Technologies, China.
Accident; analysis and prevention
|July 11, 2025
概括
本研究引入了一个空间极端值理论 (EVT) 模型,使用最大稳定过程 (MSP) 来分析交通事故风险,揭示极端交通冲突中的空间相关性,并确定外部车道和入口坡道的更高风险.
科学领域:
- 交通安全工程 交通安全工程
- 空间统计的空间统计.
- 极端价值理论是一个极端价值理论.
背景情况:
- 传统的极端价值理论 (EVT) 模型用于事故风险评估,忽视了不同区域的空间依赖.
- 积极的交通安全管理需要准确的撞车风险估计,考虑到地理差异.
研究的目的:
- 开发和验证使用最大稳定过程 (MSP) 进行空间EVT建模框架,以分析交通冲突和估计碰撞风险.
- 调查极端交通冲突的空间依赖及其对撞车风险估计的影响.
主要方法:
- 使用来自US101的NGSIM数据集的车辆轨迹数据,将碰撞时间 (TTC) 作为冲突指标.
- 应用了三种类型的MSP模型 (Schlather,Brown-Resnick,Smith) 与各种相关性和链接函数,使用对对复合概率进行估计.
- 使用极端系数来量化空间依赖和估计不同区域的撞车风险.
主要成果:
- 在极端的交通冲突中发现了显著的空间相关性,随着距离的增长而减少.
- 施拉瑟模型具有动力指数相关函数,证明了最佳的适合性,优于其他MSP模型.
- 内部车道的撞车风险低于外部车道,入口坡道的撞车风险高于出口坡道.
结论:
- 拟议的空间EVT模型有效地捕捉了极端交通冲突中的空间依赖性,改善了碰撞风险估计.
- 这些发现强调了将空间共变量纳入准确的交通安全分析的重要性.
- 该模型的结果与TTC热图相一致,验证了其用于识别高风险交通区域的可靠性.
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