An Extended Car-Following Model Considering Lateral Gap and Optimal Velocity of the Preceding Vehicle

Abstract

The car-following model (CFM) utilizes intelligent transportation systems to gather comprehensive vehicle travel information, enabling an accurate description of vehicle driving behavior. This offers valuable insights for designing autonomous vehicles and making control decisions. A novel extended CFM (ECFM) is proposed to accurately characterize the micro car-following behavior in traffic flow, expanding the stable region and improving anti-interference capabilities. Linear stability analysis of the ECFM using perturbation methods is conducted to determine its stable conditions. The reductive perturbation method is used to comprehensively describe the nonlinear characteristics of traffic flow by solving the triangular shock wave solution, described by the Burgers equation, in the stable region, the solitary wave solution, described by the Korteweg–de Vries (KdV) equation, in the metastable region, and the kink–antikink wave solution, described by the modified Korteweg–de Vries (mKdV) equation, in the unstable region. These solutions depict different traffic density waves. Theoretical analysis of linear stability and numerical simulation indicate that considering both the lateral gap and the optimal velocity of the preceding vehicle, rather than only the lateral gap as in the traditional CFM, expands the stable region of traffic flow, enhances the anti-interference capability, and accelerates the dissipation speed of disturbances. By improving traffic flow stability and reducing interference, the ECFM can decrease traffic congestion and idle time, leading to lower fuel consumption and greenhouse gas emissions. Furthermore, the use of intelligent transportation systems to optimize traffic control decisions supports a more efficient urban traffic management, contributing to sustainable urban development.