Optimal dedicated lane management for mixed traffic with connected and autonomous vehicles accounting for heterogeneous headways and speeds

Connected and autonomous vehicle (CAV) platooning, where a group of CAVs travel closely together at higher speeds, has the potential to improve both traffic capacity and free-flow speed of mixed traffic on roads. In this paper, we present a dedicated lane management framework based on an analytical understanding of mixed traffic involving CAVs and human-driven vehicles (HDVs), taking into account diverse headways, free-flow speeds, and CAV penetration rates. This framework is a bi-criteria optimization that maximizes both traffic capacity and free-flow time-mean speed of a multi-lane section, where each lane can be a non-dedicated lane, a CAV-dedicated lane, or an HDV-dedicated lane. In the capacity-maximizing case, through using both types of dedicated lanes, our approach can consistently maximize capacity across various environmental settings, such as lane numbers, CAV rates, and car-following aggressiveness. The optimal dedicated lane management scheme is summarized as follows: implement HDV-dedicated lane(s) when the total CAV ratio is low, and introduce CAV-dedicated lane(s) otherwise. The scheme aims to consolidate CAVs as much as possible to maximize the number of platooning events. In the capacity-and-speed-maximizing case, CAV-dedicated lane(s) are introduced at lower CAV penetration rates compared to the capacity-maximizing case, with greater emphasis on speed, resulting in more complete separation between CAVs and HDVs. In the bi-criteria optimization, a Pareto solution set is found, illustrating the tradeoff between two objectives, which allows transportation planners flexibility in selecting lane management strategies in accordance with operational priorities. Finally, we validate the proposed framework through agent-based simulations in VISSIM, demonstrating its effectiveness.