Impact of driving prediction on headway and velocity in car-following model under V2X environment

Abstract

The car-following model holds significant importance within the realm of the microscopic approach for examining the dynamic behavior of traffic and creating safe collaborative driving systems. Information sharing among vehicles, individuals and the surrounding environment can be made available to drivers with the help of V2X technology. Additionally, to optimize the traffic flow, it becomes essential to accurately predict the headway and velocity of leading vehicles. In this study, a car-following model is developed to analyze the impact of driving prediction on headway and velocity in V2X communication. According to the stability criteria which is discovered through linear analysis, it is observed that the stable region is more for the proposed model as compared to OV and FVD models. Nonlinear analysis is applied to derive Burger’s and mKdV equations, allowing for the exploration of triangular and kink–antikink waves in stable and unstable regions, respectively. Moreover, the stability interval of the model is determined by using the bifurcation analysis. The simulation results revealed that the stable zone enhances with the increase in the value of the prediction coefficient of headway and velocity of vehicles. Also, the numerical results are in accordance with the theoretical study. Therefore, we can depict that the proposed model is more effective in improving traffic stability due to its capability to predict future vehicle’s headway and speed.