Urban Traffic Management for Reduced Emissions: AI-based Adaptive Traffic Signal Control

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Abstract
Urban areas face significant challenges in managing traffic to reduce emissions and improve air quality. Traditional traffic management strategies often struggle to adapt to dynamic traffic conditions, leading to increased congestion and pollution. In response, researchers and policymakers are turning to innovative approaches, such as AI-based adaptive traffic signal control, to address these challenges effectively. Urban traffic management is crucial for mitigating emissions and improving air quality in densely populated areas. This research proposes an AI-based approach, namely Deep Flow (DQF), for adaptive traffic signal control to reduce emissions effectively. DQF integrates Deep Q-Learning with flow-based traffic modeling to dynamically adjust traffic signal timings based on real-time traffic flow data. To evaluate the effectiveness of DQF, extensive simulation analyses are conducted, comparing its performance with existing algorithms using appropriate metrics such as average delay, throughput, and emissions level. Results demonstrate that DQF outperforms traditional algorithms by significantly reducing emissions while maintaining efficient traffic flow. This study contributes to the advancement of eco-friendly urban traffic management strategies through innovative AI techniques.

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