贝叶斯层次非静态混合建模用于值估计在峰值超过值的方法
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
|September 25, 2025
概括
本研究引入了一种客观方法,用于在交通事故风险分析中设定值,从而提高了对主观方法的可靠性. 新的贝叶斯分层建模方法提高了碰撞估计的准确性.
科学领域:
- 交通安全和极端价值理论.
- 为运输研究开发先进的统计建模.
背景情况:
- 峰值超过值 (POT) 方法对于崩风险估计至关重要,但受到主观值选择的影响,导致结果偏差.
- 需要确定目标值,以提高极端价值理论 (EVT) 在交通安全中的应用可靠性.
研究的目的:
- 开发和验证混合建模方法,用于在事故风险估计中确定客观值.
- 为了比较五种不同的非静态贝叶斯层次混合模型 (BHHM),并确定交通冲突的最佳分布.
- 增强现有的EVT方法,以提供可靠的撞击估计.
主要方法:
- 开发了一个非静止框架,其中值随实时流量共变量而变化.
- 实现了贝叶斯层次结构,将多个站点的数据结合起来,同时考虑共变量和异质性.
- 对比了五种非静止的BHHM模型 (正常-GPD,考希-GPD,物流-GPD,马-GPD,Lognormal-GPD) 与传统方法.
主要成果:
- 拟议的BHHM方法客观地估计了值参数.
- 非静止的BHHM模型根据交通状态动态捕捉信号周期的值变化.
- 与其他BHHM模型相比,Lognormal-GPD模型展示了优越的撞击估计准确性和模型匹配.
- 由BHHM确定的值比图形诊断和量子力回归方法提供了更准确的撞击估计.
结论:
- 混合建模方法提供了一个客观和可靠的方法来确定值在事故风险分析.
- 非静止的BHHM框架提高了交通安全估计的准确性和可靠性.
- 这项研究为EVT在运输安全方面的应用提供了重大进展,提高了预测能力.
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