Driving Style Recognition of Taxi Drivers Based on Naturalistic Driving Data

To address dynamic and accurate evaluation of driving style in taxi driver safety management, this paper establishes a dynamic recognition model of driving style using a combination of unsupervised clustering and supervised classification. Based on the natural driving data of 124 taxis in Beijing for one month, the concept of vehicle operation information entropy is proposed. The driver's safety style is clustered by the K-means++ clustering algorithm to obtain three driving styles: "cautious," "aggressive," and "normal." The dynamic recognition model of driving style is established using Gradient Boosting Decision Tree (GBDT), support vector machine (SVM), and logistic regression (LR), and the effects of models are evaluated and compared. Results show that the GBDT algorithm has a better classification effect and stronger applicability to low-dimensional data. This model accurately identifies aggressive drivers. The research results provide support for drivers' safety management and targeted intervention in the taxi industry.