Integrated Routing and Intersection Control for Mixed Traffic
For traffic management in mixed-autonomy environments, this work provides a validated framework that bridges network-level routing and intersection-level control to improve overall efficiency.
This paper proposes a hierarchical framework that integrates macroscopic routing optimization with microscopic intersection control for mixed traffic of human-driven and connected automated vehicles. Simulation on the Sioux Falls network shows the integrated approach significantly improves network throughput and reduces congestion compared to either layer alone.
The rapid development of cyber-physical systems is driving a transition toward mixed traffic environments comprising both human-driven and connected and automated vehicles (CAVs). This shift presents a unique opportunity to leverage the efficient operation of CAVs to improve overall network throughput. This paper introduces a hierarchical framework designed to bridge macroscopic routing optimization at the network level with microscopic vicinity control at signalized intersections. The upper layer utilizes aggregated traffic information to provide proactive routing guidance for CAVs, aiming to minimize total travel time. The lower layer leverages local vehicle states to jointly optimize traffic light phases and individual CAV trajectories, aiming to reduce intersection crossing delays and optimize energy consumption, respectively. The effectiveness of the proposed framework is validated through SUMO on the Sioux Falls benchmark network. Results demonstrate that the integration of these macroscopic and microscopic layers yields significantly better performance compared to applying either layer in isolation, significantly improving network throughput and reducing congestion.