Multi-Agent Reinforcement Learning for Autonomous Traffic Management in Smart Cities
Abstract
The increasing urban population has led to significant traffic congestion, necessitating intelligent solutions for efficient traffic management. This paper presents a multi-agent reinforcement learning framework for autonomous traffic signal control in smart cities. The proposed system enables traffic lights to collaborate and adapt to real-time conditions, optimizing traffic flow and reducing delays. Each agent employs a reward mechanism based on vehicle throughput and waiting times, ensuring a balance between individual intersections and the overall network. Simulations on urban traffic datasets reveal that the framework significantly outperforms traditional fixed-timing and adaptive control systems, reducing congestion and emissions. The results demonstrate the potential of multi-agent systems to transform urban mobility and contribute to sustainable smart city development.
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