Adaptive Multi-Agent Reinforcement Learning for Dynamic Traffic Signal Optimization In Zero-Emission Urban Mobility

The high rates of urbanization and high concentration of vehicles have led to extreme traffic congestion, thereby contributing significantly to ‎greenhouse gas emissions. This study proposes an Adaptive Multi-Agent Reinforcement Learning (MARL) framework combined with an ‎Emission-Aware Reward System to optimize traffic lights. The suggested system in this work is decentralized MARL, where each ‎intersection is considered as a single agent that disseminates the information to the neighboring agents to prevent congestion, coordinate ‎with other agents, and attain an efficient traffic flow. This new dynamic signal timing algorithm, Traffic-Emission Adaptive Learning ‎‎(TEAL), will examine actual traffic density, vehicle types, and other environmental statistics, such as the AAI (airquake index) and carbon ‎footprint metric. The proposed module is an Eco-Mobility Prediction Module, a Graph Neural Network (GNN) predictor of congestion ‎patterns and green wave synchronization, designed to reduce vehicle idle time and emissions.‎ Furthermore, Vehicle-to-Infrastructure (V2I) is a mechanism that encourages the use of electric and hybrid vehicles, as it allows them to ‎have a higher priority in zero-emission transportation. The proposed solution is shown to reduce CO2 emissions by 30 percent and is ‎significantly more efficient than traditional models, as indicated by experimental findings conducted in a simulated urban setting. This new ‎model, therefore, scales sustainable city and zero emission mobility up to an eco-friendly solution in the future smart city environment