AI-Powered Smart Cities: Optimizing Traffic Signal Control with Cooperative Multi-Agent Reinforcement Learning

As urbanization accelerates, traffic congestion has become a major challenge in smart cities. Traditional traffic management systems, which rely on static or reactive algorithms, struggle to adapt to dynamic urban environments. Artificial Intelligence (AI), particularly Cooperative Multi-Agent Reinforcement Learning (MARL), offers a novel solution to optimizing traffic signal control. By treating traffic lights as intelligent agents that learn and collaborate in real-time, MARL-based systems can enhance traffic efficiency, minimize congestion, and improve sustainability. This paper explores the role of AI in smart city traffic management, discusses the advantages of MARL over traditional methods, and examines real-world applications. Additionally, it highlights the challenges and future research directions in implementing MARL for traffic signal control.

کلیدواژه ها:

Smart Cities ، Traffic Signal Control ، Reinforcement Learning ، Multi-Agent Systems ، Intelligent Transportation Systems