Modeling and impact analysis of connected vehicle merging accounting for mainline random length tight-platoon

Abstract

To reveal impacts of tight-platoon on connected vehicle (CV) merging, a merging advisory model is proposed to account for mainline random length tight-platoon. The tight-platoon is modeled by a consensus based algorithm with constant spacing under looking forward communication topologies, and string stabilities are analyzed to ensure robustness against gap disturbances. To ensure traffic safety, a floating merging point speed advisory model (FMP-SAM) based on finite state machine and virtual vehicle mapping is proposed to address merging conflicts. Besides, a rolling horizon control is employed in FMP-SAM to smooth speed trajectory of merging leader during gap adaption. Simulations results indicate that encouraging CV to drive in tight-platoon can improve safety, enhance mobility, conserve fuel consumption, reduce congestion and improve road capacity at higher traffic demand. Besides, FMP-SAM can improve FE performance greatly without sacrificing other performance metrics when compared with baseline merging advisory model.