Identification of Aggressive driving behavior of Online Car-hailing Drivers Based on Associations Classification

With the rapid development of online car-hailing, car-hailing-related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reducing traffic crashes. In this paper, we propose a method to recognize aggressive driving behavior based on association classification that integrates multisource features such as driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in 10-fold cross-test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule-based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification methods. Thirty-six if-then rules generated by the association classification are used to analyze the influencing factors and mechanisms of aggressive driving behavior. The study found that aggressive driving behavior is highly correlated with driver anger and disgust emotion. Aggressive driving behavior is more likely to occur when there are no passengers in the car than in the trip with passenger stage. Driver entertainment behavior and passenger interference also affect driving behavior. Drivers are prone to aggressive driving when making a U-turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of influencing factors of aggressive driving behavior and ideas for in-depth research and development of safety assistance driving devices.