Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
Accid Anal Prev. 2023 Nov;192:107244. doi: 10.1016/j.aap.2023.107244. Epub 2023 Aug 11.
The prediction of the likelihood of vehicle crashes constitutes an indispensable component of freeway safety management. Due to data collection limitations, studies have used mainly traffic flow-related variables to develop freeway crash prediction models but rarely have considered the effect of risky driving behavior on the likelihood of crashes. This study employed navigation software to collect driving behavior data and integrated multi-source data that include vehicle speed, traffic volume, and congestion index values. The study also employed the 'synthesizing minority oversampling technique and edited nearest neighbor' (SMOTE + ENN) coupled method for data balance processing. Three freeway crash likelihood prediction models were built based on the binomial logit, eXtreme Gradient Boosting (XGBoost), and support vector machine algorithms, respectively. The Shapley additive explanation (SHAP) algorithm was utilized to explore the effect of each feature variable on the likelihood of crashes. The results show that the prediction accuracy of the XGBoost model is the best of the three compared models. Under the optimal control-to-case ratio (1:1), the prediction accuracy of the XGBoost model reached 0.96 in this study, and the recall rate, specificity, and area-under-the-curve values were 0.86, 0.96, and 0.907, respectively. Comparative test results demonstrate that ranking risky driving behavior into three levels of intensity can effectively enhance the predictive accuracy of the XGBoost model. Moreover, the XGBoost model with its ten-minute time step outperformed the XGBoost model with its five-minute time step in terms of prediction accuracy. The results of the SHAP-based analysis show that the likelihood of highway crashes is high when the traffic congestion level is high and the distribution of the vehicle speed in the upstream roadway section is significant. Also, both sharp acceleration and sharp deceleration lead to greater likelihood of crashes. This paper aims to provide an effective framework for predicting and interpreting the likelihood of freeway crashes, thereby providing guidance for crash prevention, driver training, and the development of traffic regulations.
车辆碰撞的可能性预测是高速公路安全管理不可或缺的组成部分。由于数据收集的限制，研究主要使用与交通流量相关的变量来开发高速公路碰撞预测模型，但很少考虑危险驾驶行为对碰撞可能性的影响。本研究使用导航软件收集驾驶行为数据，并集成了包括车辆速度、交通量和拥堵指数值在内的多源数据。该研究还采用了“合成少数过采样技术和编辑最近邻”（SMOTE+ENN）耦合方法进行数据平衡处理。基于二项逻辑回归、极端梯度提升（XGBoost）和支持向量机算法，分别建立了三个高速公路碰撞可能性预测模型。利用 Shapley 加法解释（SHAP）算法探讨了各特征变量对碰撞可能性的影响。结果表明，与其他两个比较模型相比，XGBoost 模型的预测精度最好。在最优控制与案例比（1:1）下，本研究中 XGBoost 模型的预测精度达到 0.96，召回率、特异性和曲线下面积值分别为 0.86、0.96 和 0.907。对比测试结果表明，将危险驾驶行为分为三个强度等级可以有效提高 XGBoost 模型的预测精度。此外，在十分钟时间步长下的 XGBoost 模型比在五分钟时间步长下的 XGBoost 模型在预测精度方面表现更优。基于 SHAP 的分析结果表明，当交通拥堵水平较高且上游路段车辆速度分布显著时，高速公路碰撞的可能性较高。此外，急剧加速和急剧减速都会导致更高的碰撞可能性。本文旨在提供一种有效的预测和解释高速公路碰撞可能性的框架，为碰撞预防、驾驶员培训和交通规则制定提供指导。
