Multi-Agent Reinforcement Learning for Intelligent Traffic Management in Smart Cities

Abstract

Rapid urbanization and the continuous growth of vehicular traffic have created significant challenges for traffic management systems in modern smart cities. Traditional traffic control mechanisms based on fixed-time signal scheduling and rule-based optimization often fail to adapt efficiently to dynamic traffic conditions, resulting in traffic congestion, increased travel time, fuel consumption, environmental pollution, and reduced transportation efficiency. Intelligent Traffic Management Systems (ITMS) have emerged as a promising solution for improving urban mobility through real-time traffic monitoring, adaptive signal control, and intelligent decision-making. In recent years, Reinforcement Learning (RL) techniques have demonstrated strong capability in optimizing traffic signal control and transportation management by enabling autonomous agents to learn optimal control policies through continuous interaction with traffic environments. However, single-agent reinforcement learning approaches often exhibit limited scalability and coordination capability when deployed in large-scale urban transportation networks containing multiple intersections and dynamic traffic flows. To overcome these limitations, Multi-Agent Reinforcement Learning (MARL) has emerged as an effective framework for distributed traffic management and cooperative decision-making. In MARL based traffic systems, multiple intelligent agents collaborate to optimize traffic flow, reduce congestion, minimize vehicle waiting time, and improve transportation efficiency through coordinated learning and adaptive policy optimization. This research proposes a Multi-Agent Reinforcement Learning framework for intelligent traffic management in smart cities by integrating decentralized traffic agents, deep Q-learning optimization, cooperative policy learning, and adaptive traffic signal control mechanisms. The proposed framework utilizes traffic intersections as autonomous learning agents capable of dynamically adjusting traffic signals based on real-time traffic density, vehicle queue length, traffic flow patterns, and environmental conditions. Deep Reinforcement Learning algorithms, including Deep Q-Networks (DQN) and Actor-Critic optimization strategies, are employed to improve traffic coordination and adaptive decision-making across interconnected urban transportation networks.