Adaptive Multi-Agent Reinforcement Learning for Dynamic Traffic Signal Optimization In Zero-Emission Urban Mobility
Year: 2025
Type: article
Abstract: 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
DOI: 10.14419/qhnqst98
Authors: Aakansha Soy, Roohee Khan, Mamta Pandey
Institution: Kalinga University
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415764174
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