AI-Driven Multi-Agent Systems for Optimizing Traffic Flow and Energy Management in Smart Cities

As urban populations grow rapidly due to expanding infrastructure, cities face many new challenges related to congestion and energy use. These challenges create an urgent need for intelligent, sustainable solutions. This research paper introduces an AI-based multi-agent system (MAS) that provides a framework for optimising traffic flow and energy management simultaneously in a smart city context, supporting sustainable development goals. Each agent within the MAS is responsible for representing an entity in the city's ecosystem (vehicle, traffic signal, energy grid, building energy system, etc.) and utilizes real-time data collected locally through local decision-making processes to respond to real-time conditions in the environment around them. Using techniques from reinforcement learning, distributed coordination, and predictive modelling, the MAS autonomously responds to traffic demand and energy supply, balancing the two to minimise congestion, fuel use, and peak energy loads. The collaborative nature of the MAS enables participating agents to create tens of thousands of optimized cooperative solutions jointly. Decentralised optimisation via cooperating agents offers much greater resilience and scalability than traditional centralised control techniques. Computer simulations of realistic urban environments were used to illustrate how agent coordination with MAS technology can increase operational efficiency and support sustainable urban operations by demonstrating reduced average travel delays and energy-efficient operations. Therefore, the research indicates that intelligent MAS technology represents a premier approach to solving urban problems that are dependent on each other to implement the “Smart and Sustainable” vision for urban ecosystems.