A macroscopic Game-Theoretic Model Predictive Control framework for emergency vehicle prioritization in urban traffic networks

Efficient Emergency Vehicle (EV) prioritization in congested urban networks remains a critical challenge for smart city infrastructure, as traditional preemption methods often trigger secondary congestion and network instability. This paper proposes a decentralized, macroscopic Game-Theoretic Model Predictive Control (GTMPC) framework designed to facilitate rapid EV movement while preserving overall traffic equilibrium. The core of the proposed architecture integrates a weighted non-cooperative game with a potential function structure within a receding-horizon Model Predictive Control (MPC) structure, allowing each intersection to act as an autonomous agent that strategically allocates green-time allocations among competing traffic phases. By assigning dynamic priority weights to EV-serving approaches, the controller proactively clears queues and minimizes stop-and-go behavior. The framework is validated using high-fidelity SUMO simulations initialized with real-world traffic data from Dublin, Ireland, across varying demand profiles, including a 50% flow increase to simulate heavy traffic saturation. Experimental results demonstrate that the GTMPC framework significantly outperforms both Fixed-Time and Classical MPC strategies. Specifically, it achieves substantial reductions in Average Suffering Red-lights, Average Time Loss, and Average Stop Count, while markedly enhancing Average Speed for emergency vehicles. Notably, under heavy traffic conditions, GTMPC maintains robust performance, reducing Average Waiting Time by over 20% in scenarios where standard MPC baselines experience severe congestion and degraded traffic performance. Furthermore, the framework demonstrates an adaptive capability to manage concurrent EV arrivals through strategic multi-player negotiations. Owing to its decentralized structure, the proposed approach shows promising scalability and robustness properties in the conducted simulation studies, making it suitable for large-scale, real-time urban traffic control applications.