Jul 2026· 2026 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)· pp. 1-7· 0 citations· 20 references
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.
This article proposes a new approach to routing, termed RACER (Real-time Adaptive Congestion-aware Emergency Routing), which dynamically responds to changing traffic conditions without requiring additional traffic-signal-control infrastructure, relying instead on congestion information obtained through standard vehicle-to-infrastructure telemetry.
Harinath Ankarboina, Jasmini Kumari, Amit Kumar Singh et al.· IEEE Open Journal of the Com...· 0 citations
VeloCity is introduced, a decentralized multi-agent spatio-temporal mobility profile planning framework designed for CAVs operating in arbitrary urban areas that yields drastically lower travel times, tightly bounds delay variance, and successfully prevents congestion gridlocks even under extremely high vehicular densities.
Lorenzo Mario Amorosa, Lorenzo Farina, Vittorio Todisco et al.· 0 citations
Urban transit electrification requires balancing energy efficiency, schedule reliability, and ride comfort for electric buses (EBs), particularly when interacting with transit-priority adaptive signals in congested networks. This paper proposes PriEco-DRL, a joint optimization framework that integrates EB eco-driving with transit-priority adaptive signal control using deep reinforcement learning (DRL). The signal layer employs a priority-weighted max-pressure (Priority-MP) controller to allocate green time based on occupancy-aware pressures, while the vehicle layer adapts longitudinal control based on uncertain and dynamically evolving local signal cues. A structured reward combines guidance and event-based reinforcement to align EB arrivals with green opportunities while considering energy, time, comfort, and safety. The framework uses centralized training and decentralized execution (CTDE) with parameter sharing, allowing a single DRL agent to learn from multiple buses and routes using local observations. Experiments on a real-world corridor show that PriEco-DRL reduces EB energy consumption while maintaining network efficiency and transit priority compared with fixed-time, actuated, and rule-based signal-vehicle coordination baselines. Energy- and trajectory-based analyses reveal that the improvements stem from fewer unscheduled stop-start events and smoother speed regulation under adaptive signals. The results highlight a tunable energy-time trade-off, allowing flexible operational choices through reward weighting.
Dingshan Sun, Ang Li, Chaopeng Tan et al.· 0 citations
Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experimentation is expensive and existing approaches rely on predefined control policies that cannot capture adaptive behaviors. This paper presents an agent-based modeling (ABM) framework coupling a physical infrastructure layer, a connectivity layer (V2X), and a decision layer integrating reinforcement learning (RL) and multi-agent reinforcement learning (MARL) for platoon formation and charging coordination. We evaluate three scenarios (Baseline, Assisted, and Cognitive) using throughput, congestion, energy, emissions, and robustness metrics. Preliminary results indicate that the Cognitive scenario achieves higher throughput and lower congestion than the baseline, while the Assisted scenario delivers meaningful energy savings per kilometer through platooning. Sensitivity analysis shows that the throughput advantage of the smart corridor widens under conditions with high demand and that MARL coordination extracts greater utilization from fixed charging capacity than rule-based assignment.
Madelaine Martinez-Ferguson, Chun Wang, M. C. Camur et al.· 0 citations
