Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination
Joint Authors
Zhao, Yi
Ma, Jianxiao
Shen, Linghong
Qian, Yong
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-21
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination.
The first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division.
The second one is to optimize the JTA information transmission mode.
The third one is to optimize the operation of a single intersection.
A test network and three test groups are built to analyze the optimization effect.
Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection.
Environments with different congestion levels are also tested.
Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection.
By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.
American Psychological Association (APA)
Zhao, Yi& Ma, Jianxiao& Shen, Linghong& Qian, Yong. 2020. Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1175878
Modern Language Association (MLA)
Zhao, Yi…[et al.]. Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination. Journal of Advanced Transportation No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1175878
American Medical Association (AMA)
Zhao, Yi& Ma, Jianxiao& Shen, Linghong& Qian, Yong. Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1175878
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175878