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Title	Energy Efficient Deep Reinforcement Learning Approach To Control The Traffic Flow In Iot Networks For Smart City
ID_Doc	23240
Authors	Dhanvijay M.M.; Patil S.C.
Year	2024
Published	Journal of Ambient Intelligence and Humanized Computing, 15, 12
DOI	http://dx.doi.org/10.1007/s12652-024-04869-w
Abstract	Traffic flow control plays a crucial role in the development of smart cities, as it directly impacts various key components of urban infrastructure. With the rapid developments in artificial intelligence and large amount of data, there has been growing interest in leveraging deep reinforcement learning (RL) techniques for traffic control. These methods have shown promise in improving intersection efficiency and reducing travel time. However, existing approaches often overlook the spatial and temporal correlations between intersections, leading to suboptimal performance and increased waiting times. A decentralized traffic management system that leverages a Graph-Structured Correlation time spatial attention (GSCTSA) network and Asynchronous Advantage Actor-Critic (GSCTSA -A3C)is proposed. The system utilizes traffic controllers installed at each traffic light to collect and process real-time traffic data from nearby sensors and cameras. By processing the data at the edge layer, closer to the intersections, instead of relying on cloud-based solutions, real-time decision-making becomes feasible. The proposed GSCTSA -A3C model combines LSTM, correlational attention, and Graph Attention Networks with the Asynchronous Advantage Actor-Critic (A3C) RL algorithm. It captures spatio-temporal correlations and optimizes traffic light timings to improve traffic flow and reduce congestion. The GSCTSA module extracts essential spatio-temporal correlations, while the A3C algorithm learns optimal traffic control policies. Experimental outcomes demonstrate the effectiveness of the GSCTSA -A3C approach in achieving efficient traffic management in IoT networks for smart cities. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Author Keywords	A3C; And Deep RL; IoT; Spatial-temporal correlation; Traffic light control

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