Deep Reinforcement Learning With Multi-Agent Collaboration For Intelligent Traffic Management Systems

Abstract

With the fast-growing urbanization and vehicular traffic, the need for intelligent solutions for traffic management arises to control congestion, minimize travel delays, and improve safety. We propose DRIFT-AI (Deep Reinforcement Learning with Multi-Agent Collaboration for Intelligent Traffic Management Systems). This unique framework employs multi-agent deep reinforcement learning (MADRL) to facilitate traffic signal control optimization in complex urban networks. The proposed system treats each intersection as an intelligent agent that coordinates with neighbour agents to change signal times dynamically according to real-time traffic conditions. The developed framework combines sophisticated neural network models with spatial-temporal information, allowing it to forecast traffic volumes accurately and adapt local strategies in real-time. DRIFT-AI adopts a centralized training and decentralized execution paradigm that guarantees scalability and efficiency in large-scale networks. Key performance measures such as average traffic flow, wait time, fuel consumption, and carbon emission are assessed against state-of-the-art benchmarks. Experimental evaluations using synthetic and real-world traffic datasets show that DRIFT-AI yields substantial gains in facilitating traffic throughput growth and environmental sustainability. With the integration of reinforcement learning, resource arbitration, and multi-agent cooperation, DRIFT-AI presents a solid and scalable control mechanism for intelligent traffic systems, driving us toward smarter and more sustainable methods of urban mobility.