基于机器学习的高速公路大型货车追尾风险预测  

Prediction of rear-end collision risk of freeway trucks based on machine learning

作　　者：温惠英[1] 黄坤火 赵胜 WEN Huiying;HUANG Kunhuo;ZHAO Sheng(School of Civil Engineering and Transportation,South China University of Technology,Guangzhou Guangdong 510635,China)

机构地区：[1]华南理工大学土木与交通学院,广东广州510635

出　　处：《中国安全科学学报》2023年第9期173-180,共8页China Safety Science Journal

基　　金：国家自然科学基金资助(52172345)。

摘　　要：针对高速公路大型货车追尾事故频发的问题,评估高速公路大型货车追尾风险,并分析交通流特性对大型货车追尾风险的影响,以降低追尾事故的发生率。根据德国HighD开源数据集,以不同冲突风险等级的碰撞时间(TTC)阈值作为大型货车冲突风险的划分标准,提取大型货车的车辆轨迹与交通特征参数等数据,基于随机森林(RF)、支持向量机(SVM)和人工神经网络(ANN)等3种机器学习模型分别建立高速公路大型货车追尾风险实时预测模型;以混淆矩阵、受试者工作特征曲线的曲线下面积(AUC)和洛伦兹(KS)检验等评价指标,对比分析各模型的整体预测能力,并选取预测精度最好的模型分析各个特征参数对追尾风险的影响程度。研究结果表明:RF模型的预测准确率达75%,相对SVM模型高出8%,相对ANN模型高出10%,且RF模型的预测精确度、召回率、AUC值和KS值均优于SVM模型和ANN模型;最小车头间距、车速标准差和加速度标准差3个参数对大型货车追尾风险影响程度最高。In view of the frequent rear-end accidents of trucks on highways,the risk of rear-end collision of trucks on highways was assessed,and the influence of traffic flow characteristics was analysed to reduce the occurrence of rear-end accidents.According to the German HighD open-source dataset,the TTC thresholds of different conflict risk levels were used as the classification standard,and the vehicle trajectories and traffic parameters of trucks were extracted.The risk models of rear-end collision of trucks were established based on Random Forest(RF)model,Support Vector machine(SVM)model,and Artificial Neural Network(ANN)model respectively.The overall forecasting ability of each model was compared with the evaluation indexes such as confusion matrix,area under the receiver operating characteristic curve(AUC)and Kolmogorov-Smirnov(KS)test.The model with the best prediction accuracy was selected to analyse the influence of each characteristic parameter on the rear-end risk.The results show that the prediction accuracy of the RF model is 75%,which is 8%and 10%higher than that of the SVM model and the ANN model,respectively.The prediction accuracy,recall,AUC and KS values of the RF model are better than those of the SVM and ANN models.The three parameters of minimum headway,standard deviation of vehicle speed and standard deviation of acceleration have the highest influence on the risk of rear-end collision of trucks.

关 键 词：机器学习 高速公路 大型货车 追尾风险 预测模型 碰撞时间(TTC) 

分 类 号：X951[环境科学与工程—安全科学]