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Frontiers in Emerging Artificial Intelligence and Machine Learning

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Open Access

Smart Urban Traffic Signal Coordination System Based On Environmental Data Fusion And Machine Learning Techniques

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Dr. Haruto Nakamura 1 Dr. Yui Tanaka 1
4 Department of Intelligent Systems Tokyo Institute of Artificial Intelligence, Japan
4 School of Robotics and Machine Learning Kyoto Advanced Science University, Japan

Abstract

Urban transportation systems are increasingly challenged by congestion, emissions, and inefficient signal coordination due to rapid urbanization and growing vehicle density. Traditional traffic signal control systems rely primarily on static timing plans that fail to adapt dynamically to real-time traffic fluctuations and environmental conditions. This research proposes a Smart Urban Traffic Signal Coordination System (SUTSCS) that integrates environmental data fusion with machine learning techniques to enhance adaptive traffic management in smart cities. The system leverages multi-source data, including traffic flow metrics, air pollution indicators, and contextual environmental signals, to optimize signal timing decisions in real time. Inspired by advancements in reinforcement learning and predictive analytics, the proposed framework builds upon existing AI-driven traffic control models and extends them through environmental intelligence integration.

Recent studies emphasize the importance of sustainable urban mobility and environmental-aware traffic forecasting, where air quality and emission data contribute significantly to predictive accuracy (Shahid et al., 2021). The proposed system builds on this principle by incorporating environmental feedback loops into traffic signal optimization. Machine learning models, including deep reinforcement learning and long short-term memory networks, are utilized to predict congestion patterns and dynamically adjust signal phases. The results demonstrate that environmental data fusion significantly improves both traffic efficiency and emission reduction performance.

The study highlights the potential of integrating environmental intelligence into urban traffic systems, offering a scalable and intelligent solution for next-generation smart cities. The framework not only improves traffic flow but also contributes to sustainability goals by reducing carbon emissions and fuel consumption.

How to Cite

Nakamura, D. H., & Tanaka, D. Y. (2026). Smart Urban Traffic Signal Coordination System Based On Environmental Data Fusion And Machine Learning Techniques. Frontiers in Emerging Artificial Intelligence and Machine Learning, 3(06), 01–07. Retrieved from https://irjernet.com/index.php/feaiml/article/view/433
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