Intersection Mixed Traffic Separation Control Method: Co-Optimizing Vehicle Trajectories and Signals under Low CAV Penetration

Key words:  human-machine  mixed driving  separation, pre-signal, CAV-dedicated  lane, trajectory  optimization, signal  timing, connected  traffic

Abstract:

As Connected and Automated Vehicles (CAV) and Connected Human-driven Vehicles (CHVs) show a considerable difference in efficiency and control, currently existing measures that separate the mixed traffic at intersections often depend on CAV penetration. This dependency limits the applicability of these methods. This study presents an efficient separation control method at a low level of CAV penetration rate to overcome it. The main idea of the method is to use pre-signals to separate upstream CAVs from CHVs and then guide CAVs to dedicated lanes. In this scenario, at CAV penetration below 30%, start-up losses during the green phase can be reduced. Based on this configuration, a joint optimization model is proposed that couples vehicle trajectories with traffic signals while taking into consideration the constraints between the pre-signal and the main signal. The aim is to minimize average vehicle delay, while the particle swarm optimization algorithm is utilized to solve the model. According to simulations, the average vehicle delay is decreased by 28.27% compared to a first-come-first-served design and by 19.4% compared to a design of phase continuous tandem intersections. The CAV penetration rate, as well as the queue area length, has a significant influence on the control effectiveness. Researchers identify an optimal design value of a CAV penetration rate below 30% and a queue area length of 45 m.