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

  • Authors

    • Aakansha Soy Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Roohee Khan Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Mamta Pandey Assistant Professor, New Delhi Institute of Management, New Delhi, India
    https://doi.org/10.14419/qhnqst98

    Received date: May 2, 2025

    Accepted date: May 29, 2025

    Published date: October 31, 2025

  • Adaptive Multi-Agent Reinforcement Learning (MARL); Traffic-Emission Adaptive Learning (TEAL); Eco-Mobility Prediction Module; ‎Vehicle to Infra-structure (V2I); Graph Neural Networks (GNNs)‎.

  • 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

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  • How to Cite

    Soy, A. ., Khan , R. ., & Pandey , M. . (2025). Adaptive Multi-Agent Reinforcement Learning for Dynamic Traffic Signal Optimization In Zero-Emission Urban Mobility. International Journal of Basic and Applied Sciences, 14(SI-1), 383-389. https://doi.org/10.14419/qhnqst98