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基于SHAP-RFECV和电子收费数据的实时高速公路撞车预测:一个新的功能选择策略

Junda Huang¹, Pengpeng Xu², Kunhuo Huang¹

¹School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China.

Accident; analysis and prevention

|December 20, 2025

概括

这项研究引入了一种新的SHAP-RFECV算法,用于改进实时撞车预测. 该方法通过选择最佳特征来提高交通安全,优于传统技术.

科学领域:

交通安全与工程
机器学习在运输中的应用
数据科学用于预测建模的数据科学.

背景情况:

实时撞车预测对于积极的交通安全管理至关重要.
由于高维度,相关性和过拟合,现有模型面临特征选择方面的挑战.
特性选择对模型性能和在碰撞预测中的可解释性产生重大影响.

研究的目的:

提出和验证一种新的SHAP-RFECV功能选择算法,用于实时崩预测.
提高交通安全预测模型的准确性和可解释性.
识别影响碰撞发生的关键交通特征.

主要方法:

整合夏普利添加式扩展 (SHAP) 值与递归特征消除与交叉验证 (RFECV).
在电子收费数据上应用机器学习模型,包括XGBoost,分类增强和光梯度增强.
与基准模型和传统特征选择方法 (RFECV,LASSO,Boruta,排列重要性) 的比较.

主要成果:

SHAP-RFECV算法有效地将XGBoost的最佳特征数量从47个减少到21个.
使用SHAP-RFECV的XGBoost实现了高的样本外预测性能 (AUC: 0.8350,回忆: 73.12%,准确率: 79.54%).
与传统方法相比,SHAP-RFECV显示出更高的预测准确性和可解释性.

结论:

拟议的SHAP-RFECV算法是实时机预测中特征选择的强大而有效的方法.
前5-10分钟的平均速度,卡车流量比例和速度标准偏差是关键的撞击预测因素.
SHAP框架为交通安全分析提供了有关特征重要性和相互作用效应的宝贵见解.

关键词:

电子收费收费数据电子收费收费数据组合学习学习组合学习功能选择功能选择实时崩预测和预测.这就是 SHAP SHAP 的意思.

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