Traffic Signal Time Optimization Based on Deep Q-Network

Because cities worldwide have high population concentration, traffic congestion is a key problem that needs to be addressed. As modern technology advances, smart traffic management is able to collect data from the environment and uses a contextual signal assignment to determine the traffic flow at i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hyunjin Joo, Yujin Lim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep Q-learning reinforcement learning traffic signal control capacity SUMO Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7b4b13cb29064c038e3820bec3101b0d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7b4b13cb29064c038e3820bec3101b0d
record_format	dspace
spelling	oai:doaj.org-article:7b4b13cb29064c038e3820bec3101b0d2021-11-11T14:58:55ZTraffic Signal Time Optimization Based on Deep Q-Network10.3390/app112198502076-3417https://doaj.org/article/7b4b13cb29064c038e3820bec3101b0d2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9850https://doaj.org/toc/2076-3417Because cities worldwide have high population concentration, traffic congestion is a key problem that needs to be addressed. As modern technology advances, smart traffic management is able to collect data from the environment and uses a contextual signal assignment to determine the traffic flow at intersections and improve the traffic conditions. In this paper, we propose a green signal time allocation system based on a deep Q-network (DQN) that can maximize the capacity at intersections and assign the green light time according to the traffic conditions. The proposed system also aims to reduce the standard deviation of each lane at an intersection by considering the standard deviation of the waiting time. As a result, selfish green signal allocations can be reduced. Thus, the proposed system can achieve better experimental results in a dynamic environment than those of the green signal phase sequence allocation system.Hyunjin JooYujin LimMDPI AGarticledeep Q-learningreinforcement learningtraffic signal controlcapacitySUMOTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9850, p 9850 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep Q-learning reinforcement learning traffic signal control capacity SUMO Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	deep Q-learning reinforcement learning traffic signal control capacity SUMO Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Hyunjin Joo Yujin Lim Traffic Signal Time Optimization Based on Deep Q-Network
description	Because cities worldwide have high population concentration, traffic congestion is a key problem that needs to be addressed. As modern technology advances, smart traffic management is able to collect data from the environment and uses a contextual signal assignment to determine the traffic flow at intersections and improve the traffic conditions. In this paper, we propose a green signal time allocation system based on a deep Q-network (DQN) that can maximize the capacity at intersections and assign the green light time according to the traffic conditions. The proposed system also aims to reduce the standard deviation of each lane at an intersection by considering the standard deviation of the waiting time. As a result, selfish green signal allocations can be reduced. Thus, the proposed system can achieve better experimental results in a dynamic environment than those of the green signal phase sequence allocation system.
format	article
author	Hyunjin Joo Yujin Lim
author_facet	Hyunjin Joo Yujin Lim
author_sort	Hyunjin Joo
title	Traffic Signal Time Optimization Based on Deep Q-Network
title_short	Traffic Signal Time Optimization Based on Deep Q-Network
title_full	Traffic Signal Time Optimization Based on Deep Q-Network
title_fullStr	Traffic Signal Time Optimization Based on Deep Q-Network
title_full_unstemmed	Traffic Signal Time Optimization Based on Deep Q-Network
title_sort	traffic signal time optimization based on deep q-network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7b4b13cb29064c038e3820bec3101b0d
work_keys_str_mv	AT hyunjinjoo trafficsignaltimeoptimizationbasedondeepqnetwork AT yujinlim trafficsignaltimeoptimizationbasedondeepqnetwork
_version_	1718437901731102720

Traffic Signal Time Optimization Based on Deep Q-Network

Ejemplares similares