DT-MR-FALCON: Digital Twin–Assisted Mixed Reality Emergency Corridor Optimization for Intelligent Ambulance Navigation

Abstract

Urban traffic congestion critically impairs emergency medical services (EMS) response times, often preventing ambulances from reaching patients within the life-saving “golden hour.” Existing traffic management systems are predominantly reactive and infrastructure-focused, lacking integrated support for real-time emergency vehicle navigation. Although reinforcement learning-based signal control and Digital Twin modeling have each demonstrated promise independently, their separate deployment fails to deliver coordinated, predictive, and driveraware emergency routing. This paper presents DT-MR-FALCON, a unified framework for Emergency Corridor Optimization (ECO) that simultaneously addresses predictive traffic modeling, distributed signal coordination, and driver-centric navigation. ECO is formally defined as a dynamic, congestion-sensitive path optimization problem on urban road networks. The proposed solution integrates: (i) a Digital Twin for short-horizon traffic state forecasting, (ii) a Federated Multi-Agent Reinforcement Learning (FMARL) framework for scalable, privacy-preserving signal coordination, and (iii) a Mixed Reality (MR) interface for real-time visualization of dynamically generated emergency corridors. The framework establishes a closed-loop system coupling prediction, optimization, and human-centered decision-making, supported by theoretical guarantees on corridor optimality and delay reduction under bounded prediction error. Large-scale SUMO simulations on real-world urban networks demonstrate that DT-MR-FALCON reduces average intersection delay by 35.0%, queue length by 37.5%, and ambulance travel time by 46.2% relative to fixed-time control, achieving a 95% corridor-clearance success rate.