Distance-based time-dependent optimal ratio control scheme (TORCS) in congested mixed autonomy networks

Abstract

Connected and automated vehicle technologies provide an opportunity for a more efficient distribution of traffic in urban networks through cooperative routing behavior. Controlling the route choice of a proportion of autonomous vehicles (AVs) to establish the system optimum (SO) condition has already been investigated in static networks. This study aims to develop a time-dependent optimal ratio control scheme (TORCS) in which an optimal ratio of AVs is identified and selected to seek SO routing. The objective of the control scheme is to achieve a reasonable compromise between the system efficiency (i.e., total travel time savings) and the control cost that is proportional to the total distance traveled by SO-seeking AVs. The proposed distance-based TORCS is formulated as a bi-level optimization problem. A mixed equilibrium simulation-based dynamic traffic assignment model (SBDTA) estimates time-dependent mixed traffic patterns in the lower level based on the time-dependent optimal ratios of SO-seeking AVs obtained from the upper level. We also introduce a new concept of frequency control within the TORCS application. The frequency-based TORCS enables the central agent to control the route choice of selected AVs only in the time periods with the most significant impact on system travel time reduction. This is formulated with a binary variable within the TORCS problem indicating an action/no action regarding controlling AVs at a given assignment interval. The applicability of the model is demonstrated on a real large-scale network of Melbourne, Australia.