Car-Following Model Considering Driver's Driving Style

The research on car-following behavior aims to explore the impact of the leading vehicle's movement on the following vehicle's driving state on a one-way road. By establishing corresponding car-following models for simulation studies, it can reveal the underlying mechanism of traffic congestion, traffic flow oscillation, and other traffic phenomena, which is helpful for evaluating the stability, road capacity, and operational efficiency of traffic flow. Due to differences in driving experience, personality, and other characteristics, drivers may exhibit different car- following characteristics. Moreover, under the same conditions, the car- following behavior of different drivers may differ, and the car-following behavior of the same driver may also vary at different times. However, traditional car- following models often assume that drivers' driving behavior is homogeneous and rarely consider differences in driving styles among passing vehicles, which is inconsistent with actual situations. Therefore, this paper first extracts four driving behaviors of passing vehicles on the road (lane changing, starting, braking, and smooth driving), develops a Weight-based Adaptive Data Stream Gravity Clustering (WAStream) algorithm based on weights, and conducts clustering analysis on the time-series data of different driving behavior characteristics. Then, according to the driving style scoring model, the aggressiveness of different driving behaviors of drivers is quantified, the effective classification of driving styles of passing vehicles is achieved, and the overall driving behavior characteristics of different style driver groups are obtained. Next, by analyzing the car-following data of drivers with different styles, a speed expectation function for different style vehicles is constructed. Furthermore, the proposed car- following model considers the impact of speed and acceleration differences between the leading vehicle and multiple front vehicles in the driver's field of vision, which considers the driver's driving style. Finally, based on the NGSIM vehicle trajectory data, the key parameters of the car-following model considering the driver's driving style are calibrated using genetic algorithms, and the model's validation and numerical simulation analysis are achieved. The experimental results show that compared with the classical FVD model, the proposed car- following model can better fit the car- following data, and the MAE, MAPE, and RMSE are reduced by 1.511 m/s2, 6.122%, and 1.064 m/s2, respectively. At the same time, the model can effectively reduce the delay of vehicles in car- following behavior, construct traffic flow scenarios closer to reality, and improve the stability of traffic flow. The car- following model proposed in this study can provide effective decision- making information for transportation planning and management departments and provide model references for micro-traffic simulation studies. © 2023 Journal of Geo-Information Science. All rights reserved.