Dynamic Lane Reversal Routing and Scheduling for Connected and Autonomous Vehicles: Formulation and Distributed Algorithm

An effective intelligent transportation system is a core part of modern smart city. The Internet of Things and vehicular communication technologies facilitate rapid development of connected and autonomous vehicles (CAVs). While most studies focus on standalone CAV technologies, collective CAV control has much potential. With the connectivity and automation of CAVs, we can employ dynamic lane reversal (DLR) to optimize the travel schedules of CAVs for performance enhancement. In this paper, we propose the dynamic lane reversal-traffic scheduling management (DLR-TSM) scheme for CAVs. The system collects the travel requests from CAVs and determines their optimal schedules and routes over dynamically reversible lanes. We formulate the routing and scheduling problem on DLR as an integer linear program. To address the scaling effect, an algorithm based on alternating direction method of multipliers is designed to solve the problem in a distributed manner. We extensively evaluate the DLR-TSM and the distributed algorithm with real-world transportation data. The simulation results show that the DLR-TSM can significantly improve the travel times of CAVs and the distributed algorithm can dramatically reduce the required computational time.