Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

Gunshot detection systems (GDSs) are increasingly deployed in public, institutional, and protected areas to enable rapid, real-time response to firearm incidents. Conventional acoustic-only solutions suffer from high false-alarm rates, limited adaptability to dynamic environments, and restricted spatial awareness. Recent advances in machine learning (ML), deep learning (DL), and Internet of Things (IoT) technologies have enabled intelligent GDS capable of context-aware detection, firearm classification, and shooter localization through multimodal inputs including audio, video, and infrared data. These systems can adapt to background noise, time-of-day conditions, and deployment context, improving detection reliability while reducing latency. This survey focuses on sensor-driven, IoT-enabled gunshot detection pipelines that involve on-site sensing, edge or cloud-based inference, and operational deployment, and it does not address Internet-only information collection or general crime analytics frameworks. This survey analyzes 156 peer-reviewed studies published between 1998 and 2025, organized into six analytical dimensions: detection and modeling approaches, application contexts, hardware and deployment infrastructure, communication and security frameworks, environmental and spatial adaptability, and performance evaluation. Across these, we identify persistent challenges spanning both technical and operational domains, including accurate detection in noisy or reverberant environments, reliable localization in global positioning system (GPS)-poor or complex terrains, low-latency multimodal fusion, scalability under resource constraints, dataset scarcity, privacy and ethical considerations, vulnerability to spoofing or suppressors, and secure, real-time communication for rapid response. By consolidating technical advances and mapping them to these open problems, the review provides actionable insights for designing scalable, energy-efficient, and trustworthy GDS suitable for diverse real-world deployments.

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

Urban traffic networks face increasing challenges in optimizing signal control under dynamic and heterogeneous conditions, while adhering to real-world operational require ments such as minimum green times, pedestrian recalls, and coor dination rules. Traditional centralized approaches often struggle to adapt in real time or generalize across diverse intersections. This paper presents a decentralized graph-based multi-agent reinforcement learning (MARL) framework for adaptive traffic signal control. Each intersection operates as an independent agent with directional state representations, while a multi-head Graph Attention Network (GAT) enables efficient neighbor communication for both localized and coordinated decisions. Real-world SPaT timing rules, including green durations, clearance intervals, and pedestrian service requirements, are enforced through rule-based feasibility checks within the environment rather than learned as soft constraints. A two-stage decision process first identifies the dominant traffic direction and then determines whether to maintain or switch phases under constraint-aware action masking. The reward function penalizes vehicle delay, stops, and excessive switching. Experiments conducted using a high-fidelity simulation of a real-world urban corridor demonstrate consistent efficiency gains across varying demand levels while preserving operational feasibility. Designed for IoT-enabled deployment, the framework supports real-time controller communication via NTCIP and edge modules, while quantitative analysis of communication bandwidth, latency, and asynchronous updates is left for future field-oriented evaluation.

Adaptive traffic-signal control can respond to stochastic and directionally asymmetric demand, but transferring reinforcement-learning controllers from simulation to live infrastructure requires integration with real sensing, controller timing, pedestrian service, communication, and actuation constraints. This paper presents the field implementation and operational validation of a previously developed decentralized graph-based multi-agent reinforcement learning (DGMARL) traffic-signal controller on Chattanooga’s MLK Smart Corridor. The trained policy remained fixed during deployment and operated in inference-only mode without online learning. Field observations from computer vision, signal phase and timing (SPaT), controller-referenced timing, and neighboring agents were mapped into the policy-facing state, while live pedestrian requests and controller constraints were handled by deterministic execution logic. Policy-generated actions were validated before controller-facing signal states were transferred through an NTCIP-facing interface. Three supervised field trials across four intersections demonstrated end-to-end integration, concurrent multi-intersection execution, controller-referenced timing, configured phase and clearance progression, and retention and subsequent service of recorded pedestrian requests. For retained Trial 1 records, field-loop input-to-output timing indicators had median values of approximately 30–47 ms and 95th-percentile values of approximately 97–106 ms. The trials evaluate operational integration rather than comparative traffic-performance improvement. The results provide empirical evidence that a previously trained DGMARL framework can be transferred from a real-data-informed Digital Twin environment to live smart-corridor infrastructure while preserving the learned decision structure and accommodating field-specific sensing, timing, and controller requirements.