A Recognition Method for Risky Driving Behaviors of Urban Expressway Merging Area Based on DE-EL Model

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, enabling 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) algorithms (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 vehicle 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.