An Optimal Dynamic Lane Reversal and Traffic Control Strategy for Autonomous Vehicles

This paper studies an optimal dynamic lane reversal and traffic control (DLRTC) strategy in the presence of autonomous vehicles (AVs). A centralized controller is set to change lane directions dynamically and regulate traffic flow on a motorway network. Through vehicle to infrastructure (V2I) communication, the roadside sensors can send lane reversal information and flow control actions to the AVs which can perform lane-changing behaviors and adjust travel speed. To model the traffic dynamics under DLRTC, we propose a novel multi-lane cell transmission model (CTM). A logit model is used to characterize the lane-changing behaviors under uncontrolled cases. A mixed integer linear programming model (MILP) is formulated for DLRTC, and optimal control actions are implemented in a framework of model predictive control (MPC). The numerical experiments based on the Ayer Rajah Expressway (AYE) in Singapore are conducted to demonstrate the effectiveness of the proposed methods. The results show that the DLRTC strategy can effectively reduce road congestion and achieve better system performance compared to the benchmark method.