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Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
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Entropy 2019, 21(8), 744; https://doi.org/10.3390/e21080744
Received: 29 May 2019 / Revised: 16 July 2019 / Accepted: 27 July 2019 / Published: 29 July 2019
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies. View Full-Text
Keywords: traffic signal control; deep reinforcement learning; high-resolution data; event-based data; double dueling deep Q network traffic signal control; deep reinforcement learning; high-resolution data; event-based data; double dueling deep Q network
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Wang, S.; Xie, X.; Huang, K.; Zeng, J.; Cai, Z. Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data. Entropy 2019, 21, 744.

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Entropy, EISSN 1099-4300, Published by MDPI AG
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