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Computers, Volume 11, Issue 3

2022 March - 18 articles

Cover Story: Optimizing traffic signal control is a challenging problem, particularly when scaled to large networks. Several solutions exist for traffic signal control for small networks. However, adopting these solutions for large networks is often inefficient due to the complexity of interactions between intersections. An approach using empirical data and deep reinforcement learning can facilitate the development of intelligent solutions for large network traffic signal control. This paper presents a scalable model that relies on smart infrastructure to facilitate local data sharing and uses graph attention networks as the neural network for deep reinforcement learning. The model is expected to significantly enhance large network traffic signal performance while reducing the computational load. View this paper
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Computers - ISSN 2073-431X
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