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http://dx.doi.org/10.7471/ikeee.2019.23.4.1381

A Study on Cooperative Traffic Signal Control at multi-intersection  

Kim, Dae Ho (Dept. of Software, Gachon University)
Jeong, Ok Ran (Dept. of Software, Gachon University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1381-1386 More about this Journal
Abstract
As traffic congestion in cities becomes more serious, intelligent traffic control is actively being researched. Reinforcement learning is the most actively used algorithm for traffic signal control, and recently Deep reinforcement learning has attracted attention of researchers. Extended versions of deep reinforcement learning have been emerged as deep reinforcement learning algorithm showed high performance in various fields. However, most of the existing traffic signal control were studied in a single intersection environment, and there is a limitation that the method at a single intersection does not consider the traffic conditions of the entire city. In this paper, we propose a cooperative traffic control at multi-intersection environment. The traffic signal control algorithm is based on a combination of extended versions of deep reinforcement learning and we considers traffic conditions of adjacent intersections. In the experiment, we compare the proposed algorithm with the existing deep reinforcement learning algorithm, and further demonstrate the high performance of our model with and without cooperative method.
Keywords
Trafic signal control; Deep reinforcement learning; Deep Q Network; Coordination; Multi-intersection traffic signal control;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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