DEMTC: A Distributed Edge Computing Approach for Multi-Intersection Traffic Control

Urban traffic congestion presents significant challenges for modern cities, requiring innovative solutions for scalable and adaptive traffic control. This paper introduces DEMTC, a Distributed Edge Computing system designed for Multi-Intersection Traffic Control. DEMTC utilizes dynamic neighborhood partitioning, distributed optimization, and adaptive reinforcement learning to manage traffic across multiple intersections in real-time, effectively enhancing scalability and adaptability. Through comprehensive simulations on a 5x5 grid network, DEMTC demonstrates significant improvements, including a 25% reduction in vehicle delays, an 18% increase in throughput, and a 19% decrease in fuel consumption, outperforming traditional and centralized methods, particularly in high-traffic scenarios. Future research will focus on the real-world deployment of DEMTC, as well as its integration with multimodal transportation systems to further enhance urban traffic management and resilience.