2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 8157-8170· 0 citations· 38 references
Computer Science
TL;DR
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.
Abstract
The smooth and efficient movement of emergency vehicles in congested areas has always been a problem in intelligent transportation systems (ITS). The conventional approach to routing, typically through static shortest path calculations, often fails to respond to dynamically changing traffic conditions, leading to avoidable delays in critical situations. In this article, we propose 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 (V2I) telemetry such as roadside units or cellular reporting, which we model in SUMO via its Traffic Control Interface (TraCI). This is implemented through a combination of a proactive multi-edge look-ahead approach, a congestion-aware cost function, and a controlled approach to rerouting, ensuring stability during navigation. The proposed approach is evaluated using the SUMO microscopic traffic simulator on two large-scale real urban road networks (Bhubaneswar and Visakhapatnam), across five source and destination pairs, three congestion levels (light, moderate, severe), and 20 random seeds, for a total of 1,200 controlled runs. Because emergency-vehicle travel times are heavily right-skewed, we report the median as the primary metric alongside the mean. RACER attains the lowest median travel time across all routes and congestion levels, improving on the strongest baseline in every route, and its median travel time remains essentially flat as congestion increases (372/395/385 s for light/moderate/severe), in contrast to static Dijkstra, which degrades sharply (993/1993/3321 s). Paired statistical testing confirms the improvements over all baselines are significant ( $p\lt 10^{-29}$ ), and measured wall-clock runtime confirms the method operates in real time. We further make explicit the vehicle-to-infrastructure communication architecture on which the method operates, and show that its bounded, cooldown-gated rerouting keeps the control-plane signaling overhead low (on average fewer than three route updates per trip), making congestion-aware routing feasible over capacity-limited vehicular networks. These results demonstrate the effectiveness of incorporating congestion awareness into routing decisions, leading to faster and more reliable emergency response in such congested areas.
As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling technology for next-generation intelligent traffic management. However, fully realizing this potential is hindered by the limitations of current paradigms. Existing approaches typically optimize localized interactions rather than system-wide efficiency, incur severe communication overhead, or lack the deterministic guarantees required for safe kinematic execution. Furthermore, current multi-agent adaptations are frequently restricted to small predefined scenarios, failing to scale across large and complex urban networks. To bridge this gap, this paper introduces VeloCity, a decentralized multi-agent spatio-temporal mobility profile planning framework designed for CAVs operating in arbitrary urban areas. To minimize vehicles'travel times, VeloCity distributes mobility profile optimization directly to individual CAVs. Vehicles query a localized traffic coordinator for a reservation table, independently compute their fastest conflict-free mobility profile, and reserve their requested space-time slots back with the coordinator. By natively adapting to any arbitrary road topology, the framework manages highly irregular urban areas without requiring scenario-specific tuning, all while guaranteeing collision-free and physically executable vehicle trajectories. Extensive simulations across four large-scale real-world urban maps (Tokyo, Manhattan, Rome, and Bologna) demonstrate the framework's scalability. Compared to established state-of-the-art models, VeloCity 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
Due to increasing vehicle density, urbanization, and complex mobility patterns, road traffic injuries continue to pose a serious threat to public health and safety on a global scale. This ongoing crisis highlights the urgent need for intelligent, connected, and proactive vehicular systems that can prevent collisions, reduce injuries, and optimize traffic flow in real-time. The Internet of Vehicles (IoV) has become a key component of next-generation intelligent transportation, enabling seamless communication, data sharing, and collaborative decision-making among vehicles, roadside infrastructure, and cloud services. However, the effectiveness of current IoV communication frameworks in the real world is hampered by issues such as high latency, inefficient bandwidth utilization, limited scalability, and inadequate trust management. To address these challenges, this survey thoroughly examines 50 cutting-edge studies (2021–2025), including V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), and hybrid V2X (Vehicle-to-Everything) communication, edge–fog–cloud orchestration, 5G/6G integration, SDN (Software Defined Networking)/NFV (Network Function Virtualization) programmability, and security and trust-aware techniques. We provide a structured comparative analysis of communication types, enabling technologies, and limitations. Building on these insights, we propose an adaptive multi-tier IoV connectivity architecture that offers ultra-low latency, high scalability, and robust interoperability through distributed edge–cloud processing, AI-driven resource orchestration, adaptive blockchain-enabled security, and cross-technology communication control. Furthermore, we identify persistent research gaps and outline targeted future directions. The analysis suggests that AI-based optimization combined with hybrid and multi-tier designs has the potential to significantly improve network resilience, adaptability, and efficiency, offering a promising foundation for high-performance, secure, and reliable IoV systems.
Arbab Waheed Ahmad, M. Derawi, Raja Sana Gul· IEEE Open Journal of the Com...· 0 citations
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations
Vehicular ad hoc networks (VANETs) have emerged as a critical enabler of intelligent transportation systems, particularly when integrated with 5G infrastructure to achieve high-throughput, low-latency vehicle-to-everything (V2X) communication. Nevertheless, optimizing message routing in such environments remains a significant challenge, as the operational complexity and prohibitive cost of large-scale physical deployments severely limit empirical evaluation of alternative transmission strategies. This paper presents a stochastic Petri nets (SPNs) model for evaluating routing configurations in 5G-enabled vehicular ad hoc networks (5G-VANETs). The proposed model evaluates mean response time, drop probability, utilization, and throughput, enabling the identification of communication bottlenecks without requiring physical deployment. By abstracting the system's stochastic behavior through SPN formalism, the model supports both steady-state analysis and sensitivity evaluation under varying traffic workloads. Results demonstrate that Route 1, with direct RSU connection, achieves the lowest mean response time and highest throughput, while Route 3, which relays messages through a rear vehicle and an auxiliary RSU, yields the lowest drop probability. A sensitivity analysis based on Design of Experiments reveals that cloud capacity and cloud service time are the dominant factors affecting mean response time. The SPN model thus enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations
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.
P. Sathish, S. N· 2026 International Conferenc...· 0 citations
