Zhang Ronghui, Liu Yang, Wang Zihan, Chen Junzhou, Zeng Qiang, Zheng Lai, Zhang Hui, Pei Yulong
Guangdong Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Guangdong Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510275, Guangdong, China.
Accid Anal Prev. 2025 Mar;211:107909. doi: 10.1016/j.aap.2024.107909. Epub 2025 Jan 13.
Accurate prediction and causal analysis of road crashes are crucial for improving road safety. One critical indicator of road crash severity is whether the involved vehicles require towing. Despite its importance, limited research has utilized this factor for predicting vehicle towing probability and analyzing its causal factors. This study addresses this gap by predicting the probability of vehicle towing in road crashes based on road scene features and identifying key causal factors. Utilizing the Transportation Injury Mapping System (TIMS) dataset from California, USA, encompassing 12 years, 14 relevant features, and over 2 million road crash records, research team developed a prediction model using advanced gradient boosting techniques. Our model outperforms Random Forest, GBDT, and XGBoost in predictive accuracy. Employing the Shapley Additive Explanation (SHAP) method, researchers elucidate seven key factors influencing towing necessity. These findings introduce a novel predictive approach and offer valuable insights for road crash risk assessment and road safety planning.
道路交通事故的准确预测和因果分析对于提高道路安全至关重要。道路交通事故严重程度的一个关键指标是涉事车辆是否需要牵引。尽管其很重要，但利用这一因素来预测车辆牵引概率并分析其因果因素的研究有限。本研究通过基于道路场景特征预测道路交通事故中车辆牵引的概率并识别关键因果因素来填补这一空白。研究团队利用美国加利福尼亚州的交通伤害地图系统（TIMS）数据集，该数据集涵盖12年、14个相关特征以及超过200万条道路交通事故记录，使用先进的梯度提升技术开发了一个预测模型。我们的模型在预测准确性方面优于随机森林、梯度提升决策树（GBDT）和极端梯度提升（XGBoost）。研究人员采用夏普利值附加解释（SHAP）方法，阐明了影响牵引必要性的七个关键因素。这些发现引入了一种新颖的预测方法，并为道路交通事故风险评估和道路安全规划提供了有价值的见解。
