An extended car-following model considering the effect of driving prediction on headway and velocity variation under V2X environment

Vehicle-to-everything (V2X) communication technology is regarded as a promising technology to optimize traffic flow and improve traffic safety. In this paper, an extended car-following model is proposed to simulate traffic flow by considering the effect of driving prediction on headway and velocity variation under V2X environment. The stability condition of the new model is obtained by applying the linear stability theory. Compared with the full velocity difference (FVD) model, the stable region of the new model can be significantly enlarged on the phase diagram, and the predictive headway and velocity variation effect can further enhance the traffic stability. The mKdV equation is derived to describe the evolution characteristics of traffic density waves by using the reductive perturbation method. The results show that predicting future vehicle’ headway and velocity variation can effectively suppress traffic congestion, reduce energy consumption and improve the stability of the traffic system. Theoretical analysis and simulation results verify the feasibility and validity of the predictive headway and velocity variation effect on the improvement of traffic flow under V2X environment.