Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6489027 |
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| _version_ | 1850218764520390656 |
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| author | Yi Zhao Jianxiao Ma Linghong Shen Yong Qian |
| author_facet | Yi Zhao Jianxiao Ma Linghong Shen Yong Qian |
| author_sort | Yi Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7aab807ba6cc4c9db038d2d2085800ff |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7aab807ba6cc4c9db038d2d2085800ff2025-08-20T02:07:37ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/64890276489027Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal CoordinationYi Zhao0Jianxiao Ma1Linghong Shen2Yong Qian3College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaThis 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.http://dx.doi.org/10.1155/2020/6489027 |
| spellingShingle | Yi Zhao Jianxiao Ma Linghong Shen Yong Qian Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination Journal of Advanced Transportation |
| title | Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination |
| title_full | Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination |
| title_fullStr | Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination |
| title_full_unstemmed | Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination |
| title_short | Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination |
| title_sort | optimizing the junction tree based reinforcement learning algorithm for network wide signal coordination |
| url | http://dx.doi.org/10.1155/2020/6489027 |
| work_keys_str_mv | AT yizhao optimizingthejunctiontreebasedreinforcementlearningalgorithmfornetworkwidesignalcoordination AT jianxiaoma optimizingthejunctiontreebasedreinforcementlearningalgorithmfornetworkwidesignalcoordination AT linghongshen optimizingthejunctiontreebasedreinforcementlearningalgorithmfornetworkwidesignalcoordination AT yongqian optimizingthejunctiontreebasedreinforcementlearningalgorithmfornetworkwidesignalcoordination |