Combining Cooperative Re-Routing with Intersection Coordination for Connected and Automated Vehicles in Urban Networks

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information and the fundamental diagrams of each network edge. This re-routing approach predicts when each edge will reach critical density and proactively adjusts the routing algorithm's weights to prevent congestion before it occurs. The low-level controller coordinates CAVs as they cross signal-free intersections, generating optimal, fuel-efficient trajectories while ensuring safe passage by satisfying all relevant constraints. We formulate the problem as an optimal control problem and derive an analytical solution. Using the SUMO micro-simulation platform, we conduct simulation experiments on a realistic network. The results show that our hierarchical framework significantly enhances network performance compared to a baseline static routing approach. By dynamically re-routing vehicles, our approach successfully reduces total travel time and mitigates congestion before it develops.