交通信息与安全2024，Vol.42Issue(6)：23-30,8.DOI:10.3963/j.jssn.1674-4861.2024.06.003
基于DE-EL的城市快速路合流区危险驾驶行为识别方法
A Recognition Method for Risky Driving Behaviors of Urban Expressway Merging Area Based on DE-EL Model
摘要
Abstract
A method for recognizing risky driving behaviors using vehicle trajectory data is established to improve safety and prevent traffic accident in urban expressway merging areas.The characteristic thresholds of four types of risky driving behaviors are firstly determined using a risk assessment approach and the interquartile range method.Subsequently,drivers'risk scores(G)are calculated using the established spectrum of risky driving behaviors,en-abling the classification of drivers as safe or risky.To balance the datasets,the driving risk samples are augmented by data equalization(DE)algorithms(ROS,ADASYN,and SMOTE).Combining ensemble learning(EL)algo-rithms(XGBoost,LGBM and AdaBoost)to build various DE-EL models for risky driving behaviors recognition.The Spearman correlation coefficient is used to optimize the input feature parameters,which include five categories:vehicle speed,acceleration and deceleration,lateral operation,position characteristics and time occupation ratio.The optimal recognition model is is determined based on precision rate,recall rate,F1-score and AUC value.The results show that the level of driver risk is most strongly correlated with driver lateral operation and less so with ve-hicle speed in merging areas.The unbalanced trajectory dataset makes it difficult to effectively identify risky driving behaviors by the EL algorithm,while the DE algorithm can improve the properties of the classification algorithm.After optimizing the input feature parameters,the performance of the DE-EL recognition model improves,and the SMOTE-LGBM model is the best one with precision rate of 93.4%,recall rate of 92.1%,F1-score of 0.927,and AUC value of 0.933.This model is applicable for recognizing,warning,intervening in risky driving behaviors in merging areas.关键词
交通安全/合流区驾驶行为/危险驾驶行为谱/集成学习/数据均衡算法Key words
traffic safety/driving behaviors in merging areas/risky driving behavior spectrum/ensemble learning/data equalization algorithm分类
交通工程引用本文复制引用
谢厅,刘星良,刘唐志,徐进..基于DE-EL的城市快速路合流区危险驾驶行为识别方法[J].交通信息与安全,2024,42(6):23-30,8.基金项目
国家自然科学基金项目(52172341)、重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0519、CSTB2022NSCQ-MSX1516)资助 （52172341）
