一种可解释的动态组合选择多类失衡方法与组合失衡学习,用于预测道路交通事故伤害严重程度
Kamran Aziz1, Feng Chen2, Mahmood Ahmad3,4
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao'an Road, Jiading, Shanghai, 201804, China. kamran_aziz@tongji.edu.cn.
Scientific reports
|July 9, 2025
概括
准确的碰撞伤害预测得到了改进,使用了一种新的方法,贝叶斯优化动态组合选择多类失衡 (DES-MI) 和组合失衡学习 (EIL). 这种方法解决了多类不平衡,以获得更好的道路安全见解.
科学领域:
- 交通安全与可持续交通运输
背景情况:
- 准确的碰撞伤害严重程度预测对于道路安全和可持续运输至关重要.
- 机器学习模型经常与多类不平衡作斗争,阻碍了交通风险评估.
- 多类失衡是交通事故分析的一个重大挑战,经常被忽视.
研究的目的:
- 提出一种新的方法,DES-MI与EIL,用于准确估计多类事故伤害和严重程度.
- 为了应对交通风险评估中多类失衡的挑战.
- 为了提高不平衡伤害严重程度的数据集的分类性能.
主要方法:
- 开发了贝叶斯优化动态组合选择多类不平衡 (DES-MI).
- 集成整体失衡学习 (EIL) 产生基础分类器.
- 使用EIL分类器的同质和异质池与DES-MI进行最佳选择.
主要成果:
- 配合EIL的DES-MI显著提高了对多类不平衡数据集的分类性能.
- 具有异质EIL的DES-MI表现出卓越的性能.
- 带有BRF的DES-MI在同质组合中显示出了显著的结果.
- SHAP分析确定了关键变量:性别,年龄,月份,安全气囊部署和道路形状.
结论:
- 拟议的DES-MI模型与EIL分类器有效地解决了多类失衡的撞击伤害严重程度预测问题.
- 这种方法为道路交通安全的利益相关者提供了有价值的见解.
- 支持开发更安全,更有效,更可持续的城市公路运输系统.
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