Stability and extension of a car-following model for human-driven connected vehicles

Despite the prosperous development of connected vehicle (CV) and its car-following (CF) models, human-driven CV and its CF properties are rarely investigated. This paper studies the stability characteristics of and then extends a recently-developed CF model of human-driven CV which incorporates human factors (CV-CF hereafter) by considering two levels of driver compliance, i.e., low compliance and high compliance. First, we investigate the stability of the CV-CF model, and validate the results with the simulation experiments. We then assess CV's impact on the mixed traffic flow by deriving a stability criterion of heterogeneous traffic with the Laplace transform based method and analysing the influence of different levels of connectivity and their penetration rates. Furthermore, we extend the CV-CF model by considering two important additional human factors, i.e., time delay and estimation error, and evaluate different human factors' impact on the stability and oscillation characteristics of the CV-CF model. The results reveal that the connected environment indeed promotes the CF stability and alleviates traffic congestion, and that higher compliance to the information provided is generally more beneficial to the stability of traffic flow, except the situation with a large time delay.

Automated summary

This study investigates the stability characteristics of human-driven connected vehicles (CV) and extends a recently-developed car-following (CF) model by considering human factors. The results show that the connected environment improves CF stability and reduces traffic congestion, and higher compliance to information benefits traffic flow stability, except in situations with large time delays.