数据转换对道路交通中的严重事件预测的影响,使用极端价值理论
Zhankun Chen1, Carl Johnsson1, Carmelo D'Agostino1
1Department of Technology & Society, Lund University, Lund 221 00, Sweden.
Accident; analysis and prevention
|August 12, 2025
概括
极端价值理论 (EVT) 通过使用替代安全措施 (SMoS) 提高了事故预测. 这项研究严格分析了SMoS的转换,以改进严重交通相互作用的数学建模,并预测事故频率.
科学领域:
- 交通安全研究 交通安全研究
- 极端事件的数学建模.
- 运输工程 运输工程 运输工程
背景情况:
- 极端价值理论 (EVT) 是一种在微观水平上预测交通事故频率的领先方法.
- 替代安全措施 (SMoS) 量化了道路使用者的近距离,近距离下降表明碰撞风险更高.
- 极端相互作用的建模需要SMoS的变化减小,但预测的准确性取决于所选择的转换方法.
研究的目的:
- 在极端值理论 (EVT) 的框架内,严格制定线性和非线性转换对替代安全措施 (SMoS) 的影响.
- 评估不同的SMoS转换如何影响极端交通事件和事故频率的预测.
- 通过数学解释交通冲突和交通事故之间的基本关系.
主要方法:
- 应用尾部分析理论来制定SMoS转换的影响.
- 在瑞典交通交互数据集上测试该方法.
- 使用经验贝叶斯校正的事故模型评估预测性能.
主要成果:
- 该研究提供了严格的数学解释,说明各种线性和非线性转换如何影响使用EVT的极端交通相互作用的建模.
- 分析表明,这些变化对预测严重事件的准确性和整体事故频率的影响.
- 这些发现强调了选择适当的转型对于有效的前性交通安全分析的重要性.
结论:
- 严格的SMoS转换公式提供了更深入地了解它们在基于EVT的事故预测中的作用.
- 该研究的方法可以扩展到建立一个标准程序来建模交通冲突和预测事故.
- 这项研究通过完善冲突-碰撞关系建模的数学基础,有助于推进主动交通安全.
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