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Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG

As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional tr...

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Detalles Bibliográficos
Autores principales: Zheng, Guorong, Liu, Yuke, Fu, Yazhou, Zhao, Yingjie, Zhang, Zundong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535160/
https://www.ncbi.nlm.nih.gov/pubmed/37766030
http://dx.doi.org/10.3390/s23187975
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author Zheng, Guorong
Liu, Yuke
Fu, Yazhou
Zhao, Yingjie
Zhang, Zundong
author_facet Zheng, Guorong
Liu, Yuke
Fu, Yazhou
Zhao, Yingjie
Zhang, Zundong
author_sort Zheng, Guorong
collection PubMed
description As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional traffic management. Addressing regional traffic optimization and control methods based on the characteristics of regional congestion has become a crucial and complex issue in the field of traffic management and control research. This paper focuses on the macroscopic fundamental diagram (MFD) and aims to tackle the control problem without relying on traffic determination information. To address this, we introduce the Q-learning (QL) algorithm in reinforcement learning and the Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning. Subsequently, we propose the MFD-QL perimeter control model and the MFD-DDPG perimeter control model. We conduct numerical analysis and simulation experiments to verify the effectiveness of the MFD-QL and MFD-DDPG algorithms. The experimental results show that the algorithms converge rapidly to a stable state and achieve superior control effects in optimizing regional perimeter control.
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spelling pubmed-105351602023-09-29 Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG Zheng, Guorong Liu, Yuke Fu, Yazhou Zhao, Yingjie Zhang, Zundong Sensors (Basel) Article As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional traffic management. Addressing regional traffic optimization and control methods based on the characteristics of regional congestion has become a crucial and complex issue in the field of traffic management and control research. This paper focuses on the macroscopic fundamental diagram (MFD) and aims to tackle the control problem without relying on traffic determination information. To address this, we introduce the Q-learning (QL) algorithm in reinforcement learning and the Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning. Subsequently, we propose the MFD-QL perimeter control model and the MFD-DDPG perimeter control model. We conduct numerical analysis and simulation experiments to verify the effectiveness of the MFD-QL and MFD-DDPG algorithms. The experimental results show that the algorithms converge rapidly to a stable state and achieve superior control effects in optimizing regional perimeter control. MDPI 2023-09-19 /pmc/articles/PMC10535160/ /pubmed/37766030 http://dx.doi.org/10.3390/s23187975 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zheng, Guorong
Liu, Yuke
Fu, Yazhou
Zhao, Yingjie
Zhang, Zundong
Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title_full Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title_fullStr Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title_full_unstemmed Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title_short Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG
title_sort perimeter control method of road traffic regions based on mfd-ddpg
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535160/
https://www.ncbi.nlm.nih.gov/pubmed/37766030
http://dx.doi.org/10.3390/s23187975
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