Deep Q-network-based traffic signal control models.

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic si...

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Autores principales:	Sangmin Park, Eum Han, Sungho Park, Harim Jeong, Ilsoo Yun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f9b604ae37e4356b07a8755728d2c30
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id	oai:doaj.org-article:7f9b604ae37e4356b07a8755728d2c30
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spelling	oai:doaj.org-article:7f9b604ae37e4356b07a8755728d2c302021-12-02T20:08:39ZDeep Q-network-based traffic signal control models.1932-620310.1371/journal.pone.0256405https://doaj.org/article/7f9b604ae37e4356b07a8755728d2c302021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256405https://doaj.org/toc/1932-6203Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.Sangmin ParkEum HanSungho ParkHarim JeongIlsoo YunPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sangmin Park Eum Han Sungho Park Harim Jeong Ilsoo Yun Deep Q-network-based traffic signal control models.
description	Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.
format	article
author	Sangmin Park Eum Han Sungho Park Harim Jeong Ilsoo Yun
author_facet	Sangmin Park Eum Han Sungho Park Harim Jeong Ilsoo Yun
author_sort	Sangmin Park
title	Deep Q-network-based traffic signal control models.
title_short	Deep Q-network-based traffic signal control models.
title_full	Deep Q-network-based traffic signal control models.
title_fullStr	Deep Q-network-based traffic signal control models.
title_full_unstemmed	Deep Q-network-based traffic signal control models.
title_sort	deep q-network-based traffic signal control models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7f9b604ae37e4356b07a8755728d2c30
work_keys_str_mv	AT sangminpark deepqnetworkbasedtrafficsignalcontrolmodels AT eumhan deepqnetworkbasedtrafficsignalcontrolmodels AT sunghopark deepqnetworkbasedtrafficsignalcontrolmodels AT harimjeong deepqnetworkbasedtrafficsignalcontrolmodels AT ilsooyun deepqnetworkbasedtrafficsignalcontrolmodels
_version_	1718375192312414208

Deep Q-network-based traffic signal control models.

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