创新的预测和事故车辆拖拉概率的因果分析使用先进的梯度增强技术在广泛的道路交通现场数据上的先进梯度增强技术
Ronghui Zhang1, Yang Liu1, Zihan Wang1
1Guangdong Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Accident; analysis and prevention
|January 14, 2025
概括
这项研究使用道路现场数据预测了道路事故后车辆拖车的概率. 它确定了影响拖车需求的关键因素,改善了道路安全分析和规划.
科学领域:
- 交通运输安全运输安全
- 交通事故分析分析
- 预测建模的预测建模.
背景情况:
- 预测道路事故的严重程度对于安全至关重要.
- 车辆拖车是事故严重性的关键指标.
- 关于预测拖车概率及其原因的研究有限.
研究的目的:
- 使用道路场景特征预测车辆在道路事故中的拖车概率.
- 确定影响车辆拖车必要性的关键因果因素.
- 解决用于道路安全的拖车数据的研究缺口.
主要方法:
- 使用运输伤害映射系统 (TIMS) 数据集 (12年,加利福尼亚州).
- 使用先进的梯度增强技术开发了一个预测模型.
- 用Shapley添加式解释 (SHAP) 进行因果因子分析.
主要成果:
- 梯度提升模型表现出比随机森林,GBDT和XGBoost更高的预测准确度.
- 确定了七个影响车辆拖车必要性的关键因素.
- 该模型提供了一种新的方法来预测拖概率.
结论:
- 这项研究提供了一种新的方法,用于预测车辆在道路事故中拖车的概率.
- 鉴定的因果因素为道路安全规划和风险评估提供了宝贵的见解.
- 通过数据驱动的分析,研究结果有助于提高整体道路安全.
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