Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles

Abstract

The emerging connected autonomous vehicle (CAV) technology offers a significant opportunity to address the congestion problem caused by the selfish routing behavior of human drivers. If the transportation management center (TMC) can control the routing of all vehicles on the network, they could easily reverse this effect. However, during the transition period, CAVs will share roads with human-piloted vehicles (HVs). In the era of mixed autonomy, a more realistic scenario is to route the CAVs to lead the traffic state towards the desired equilibrium. We first consider a repeated routing game in which the TMC can control the routes of CAVs to minimize the system travel time, i.e., CAVs follow the system optimal (SO) routing principle. In this manner, we can influence the travel time traversing each link, which indirectly influences HV users’ route choices. As a consequence, HV users may adjust their routes dynamically through day-to-day (DTD) learning to minimize individual travel cost. On the other hand, the SO routing strategy may induce higher travel time for CAVs than HVs within the same origin–destination (OD) pair. To address such inherent unfairness, road pricing is used to influence route choices of HVs to achieve specific supply regulation targets, e.g., traffic restraint. We present qualitative analysis on the stability of the controlled DTD dynamics and the effect of road pricing regarding the unfairness. To the best of our knowledge, this paper is one of the first to design dynamic pricing schemes for a mixed autonomy system under DTD learning dynamics. We carry out numerical experiments to demonstrate the effectiveness of the control schemes. The cooperative CAVs could reduce the total travel time when the market penetration rate of CAVs reaches the lower minimum control ratio that the network could escape the user equilibrium (UE) condition. When the market penetration rate of CAVs reaches the upper minimum control ratio, the network would achieve SO condition even though there are non-cooperative HVs. On the other hand, the pricing scheme can reduce the unfairness index and total travel time of the network when the market penetration rate of CAVs is low.