Affiliated with RC | false |
Status | 已發表Published |
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning | |
Li,Zhenning1; Yu,Hao2; Zhang,Guohui3; Dong,Shangjia4; Xu,Cheng Zhong1![]() | |
2021-04 | |
Source Publication | Transportation Research Part C: Emerging Technologies
![]() |
ISSN | 0968-090X |
Volume | 125 |
Abstract | Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep Deterministic Policy Gradient) to achieve optimal control by enhancing the cooperation between traffic signals. By introducing the knowledge-sharing enabled communication protocol, each agent can access to the collective representation of the traffic environment collected by all agents. The proposed method is evaluated through two experiments respectively using synthetic and real-world datasets. The comparison with state-of-the-art reinforcement learning-based and conventional transportation methods demonstrate the proposed KS-DDPG has significant efficiency in controlling large-scale transportation networks and coping with fluctuations in traffic flow. In addition, the introduced communication mechanism has also been proven to speed up the convergence of the model without significantly increasing the computational burden. |
Keyword | Adaptive Traffic Signal Control Deep Learning Knowledge Sharing Multi-agent Reinforcement Learning Transportation Network |
DOI | 10.1016/j.trc.2021.103059 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Transportation |
WOS Subject | Transportation Science & Technology |
WOS ID | WOS:000636374400006 |
Scopus ID | 2-s2.0-85101941829 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | University of Macau |
Corresponding Author | Yu,Hao |
Affiliation | 1.State Key Laboratory of Internet of Things for Smart City,University of Macau,Macao 2.School of Transportation,Southeast University,China 3.Department of Civil and Environmental Engineering,University of Hawaii at Manoa,United States 4.Department of Civil and Environmental Engineering,University of Delaware,United States |
First Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Li,Zhenning,Yu,Hao,Zhang,Guohui,et al. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies,2021,125. |
APA | Li,Zhenning,Yu,Hao,Zhang,Guohui,Dong,Shangjia,&Xu,Cheng Zhong.(2021).Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning.Transportation Research Part C: Emerging Technologies,125. |
MLA | Li,Zhenning,et al."Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning".Transportation Research Part C: Emerging Technologies 125(2021). |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment