Optimization-based Urban Network Traffic Management with Mixed Autonomy Incorporating Dynamic Saturation Rates

This work introduces a novel optimization-based control framework for managing traffic flow in a network with mixed autonomy, where both Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles coexist. The proposed model extends the store-and forward model by incorporating a dynamic saturation flow rate, which considers the autonomy level of queues. The problem is formulated as a non-convex Quadratic Program (QP), which accounts for the dynamic aspects of the traffic network in terms of queue lengths, spillback, green time allocation, routing of CAVs, and dynamic saturation flow rate. To solve the nonconvex QP problem, we employ a computationally efficient heuristic algorithm, which treats the dynamic saturation flow rate as a parameter outside the optimization framework, converting the non-convex problem into a series of convex subproblems. Numerical results on a grid network demonstrate the performance of the proposed methodology.