Deep Reinforcement Learning for Autonomous Traffic Signal Control in Smart Cities
Abstract
Efficient traffic signal control is essential for reducing congestion and emissions in urban areas. This paper presents a deep reinforcement learning (DRL) approach for autonomous traffic signal control, leveraging multi-agent systems to optimize traffic flow. The model incorporates real-time traffic data and simulates dynamic interactions between intersections. Experiments using synthetic and real-world datasets from smart cities demonstrate significant reductions in travel time, fuel consumption, and CO2 emissions, highlighting the potential of DRL for urban traffic management.
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