Neuro-Adaptive Swarm Control: A Decentralized AI Approach for Intelligent Traffic Management

Urban traffic congestion requires intelligent solutions beyond traditional rule-based systems. This paper presents Neuro-Adaptive Swarm Control, a decentralized AI approach that combines swarm intelligence with adaptive neural networks to improve smart traffic management. Inspired by self-organizing biological systems, it enables real-time coordination between traffic signals, autonomous vehicles, and smart infrastructure without relying on centralized control. Using multi-agent reinforcement learning (MARL) and neural adaptation, the system allows traffic entities to learn, predict congestion, and optimize responses dynamically. Unlike conventional AI-based traffic control, which relies on precollected data, this approach continuously adapts to live traffic conditions, enhancing scalability and efficiency. Theoretical analysis and simulations demonstrate that this model reduces congestion, improves traffic flow, and minimizes energy consumption, making it a scalable and robust solution for future smart cities.