A car-following model of CAVs integrating state information from multiple leading and single following vehicles

To uncover the root causes of congestion in traffic flows involving connected and autonomous vehicles (CAVs) and improve stability, the multi-leading and single-following vehicles state information car-following (MLSFICF) model for CAVs is introduced in this study. The model is based on the classical optimal velocity car-following framework and its improved 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 critical stability conditions of the model, and nonlinear analysis derives the modified Korteweg-de Vries (mKdV) equation to describe the evolution of traffic congestion near stability critical points. Numerical simulations indicate that the MLSFICF model achieves superior stability compared with the FVD, FVDA, BLVD, MHVD, MHVDA, and ACC/CACC models from the PATH laboratory. Under mixed traffic conditions, higher CAV penetration rates progressively increase traffic stability and reduce congestion. The model can be applied to traffic flow simulations involving CAVs, offering both a theoretical foundation and a modeling framework for optimizing traffic flow and developing control strategies.