A car following model in the context of heterogeneous traffic flow involving multilane following behavior

Abstract

For improving the stability of heterogeneous traffic flow in smart grids, the Multi-Lane Multi-Vehicle State Information Index Smoothing Fusion Car following model in the context of heterogeneous traffic flow (MLMVISF model) is proposed for CAVs (connected and autonomous vehicles) and HDVs (human driving vehicles). The model takes into account the combined effects of rearview effects, state information (e.g., velocity and acceleration) of multiple preceding vehicles, average velocity information of adjacent lanes, and the perception error headway. Through linear stability analysis, the model's stability criteria were investigated, and the optimal parameters were determined. MLMVISF model improves heterogeneous traffic flow stability based on linear stability and numerical simulation analysis. MLMVISF improves stability by 15.34 % over the FVD model (Full velocity difference) and 10.43 % over the MVISF model (Multi Vehicle Information Smooth Fusion). The study contributes to the design of automated driving strategies for smart grids.