A Car-following Model for CAVs Integrating State Information from Multiple Leading and Single Following Vehicles

To uncover the root causes of congestion in traffic flow involving connected and autonomous vehicles (CAVs) and improve stability, this study introduces the Multi-Leading and Single-Following Vehicles State Information Car-Following (MLSFICF) model for CAVs. The model is based on the classical optimal velocity car-following framework and its advanced extensions, integrating the combined effects of complete state information from multiple leading vehicles and a single following vehicle, such as headway, velocity differences, and acceleration. Linear stability analysis is used to determine the model's critical stability conditions, while nonlinear analysis derives the Korteweg-de Vries (mKdV) equation to describe the evolution of traffic congestion near these critical points. Numerical simulations indicate that the MLSFICF model achieves superior stability compared to the FVD, FVDA, BLVD, MHVD, and ACC/CACC models from the PATH laboratory. In mixed traffic conditions, higher CAV penetration rates progressively enhances traffic stability and reduces congestion. This model offers a theoretical foundation and practical guidance for simulating traffic flow involving CAVs, facilitating effective traffic management and congestion mitigation.