基于增强的MSCPO-XGBoost混合模型的交通事故严重程度预测
Fei Chen1, Xiang Qun Liu1, Jian Jun Yang2,3
1School of Automobile and Transportation, Xihua University, Chengdu, 610039, China.
Scientific reports
|July 16, 2025
概括
这项研究引入了一种新的AI模型,用于预测中国的道路交通事故严重程度,改善交通安全. 使用XGBoost的修改后的随机猪优化器 (MSCPO) 提高了预测的准确性.
科学领域:
- 交通安全 交通安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在中国,道路交通事故是公众安全的一个主要问题.
- 准确预测事故严重程度对于有效的交通管理和政策制定至关重要.
研究的目的:
- 开发和评估一个新的框架来预测道路交通事故严重程度.
- 为了优化XGBoost算法,使用一个修改的随机形猪优化器 (MSCPO).
主要方法:
- 利用来自中国国家汽车事故深入调查系统 (NAIS) (2018-2023) 的4287起事故案件数据集.
- 为了解决培训数据中的阶级不平衡,应用了合成少数群体过量采样技术 (SMOTE).
- 使用MSCPO算法来优化XGBoost的超参数,以提高预测性能.
主要成果:
- 拟议的MSCPO-XGBoost模型实现了高性能指标:准确率为83.57%,回忆率为85.23%,F1得分为84.30%和AUC为92.82%.
- 对比实验显示MSCPO-XGBoost模型的表现优于SVM,随机森林,BP神经网络和CNN.
- SHAP分析确定了事故严重性的关键预测因素,包括发动机排量,车辆质量,交通信号和驾驶员年龄.
结论:
- 该MSCPO-XGBoost框架在道路交通事故严重程度预测方面取得了重大进展.
- 这些发现为制定交通安全政策和开发智能交通管理系统提供了宝贵的见解.
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