Prediction and Analysis of Dangerous Car-Following Behavior Based on Trajectory Data

During the driving process, drivers’ misjudgment and improper operation are highly likely to lead to traffic accidents. This article aims to explore the mechanism of dangerous car following behavior and construct a predictive model for dangerous car following behavior that considers multiple factors such as environment and vehicle interaction. First, we propose a car following driving behavior extraction method based on trajectory data, establish a three-dimensional driving behavior feature set of vehicle motion characteristics, vehicle interaction characteristics and microscopic traffic flow characteristics, and use random forest model to screen key features. Subsequently, the K-means algorithm was used to optimize the unbalanced dataset of dangerous car following behavior, and a dangerous car following behavior prediction model based on K-GMMHMM was proposed, and the prediction accuracy was compared with other models. The prediction performance of K-GMMHMM was found to be better, with an accuracy rate of 97.86%, verifying the effectiveness of the proposed method for predicting dangerous car following behavior. This provides theoretical support for the development and application of vehicle warning assistance systems, and has practical significance and application value for improving road traffic safety and preventing traffic accident risks.