A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region
Urban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control s...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5681234 |
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| _version_ | 1832561960880701440 |
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| author | Lingyu Zheng Bing Wu |
| author_facet | Lingyu Zheng Bing Wu |
| author_sort | Lingyu Zheng |
| collection | DOAJ |
| description | Urban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. However, those studies are regardless of the traffic flow theory of the network and the road users’ perspectives on the performance of traffic. This study proposes a multiagent reinforcement learning (MARL) based traffic control strategy, in which each intersection in a macroscopic fundamental diagram (MFD) region was controlled by one agent using the level of services (LOS) and MFD-based parameters as rewards. The proposed MARL strategy was evaluated by simulation in a 3×3 grid network compared with pretimed, actuated, and MFD-based traffic control strategies. The evaluation results showed that, at different demand levels, the proposed MARL strategy outperforms the other three traffic control strategies in terms of average intersection queue length and average intersection waiting time to a different extent. Results also showed that the proposed MARL dissipated the congestion faster than the other three control strategies. Results of the Friedman test indicated that the differences in performances between the proposed MARL and other strategies were statistically significant regardless of the demand level. The MFD in the testbed network controlled by the proposed MARL was different from that controlled by the pretimed strategy, especially the MFD scatter plot. It provides insights on considering the traffic flow theory of the network when applying MARL to traffic control strategies. |
| format | Article |
| id | doaj-art-1814dc724efd41d9a8e2090eb772615c |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1814dc724efd41d9a8e2090eb772615c2025-02-03T01:23:38ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5681234A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram RegionLingyu Zheng0Bing Wu1College of Transport and CommunicationsKey Laboratory of Road and Traffic Engineering of Ministry of EducationUrban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. However, those studies are regardless of the traffic flow theory of the network and the road users’ perspectives on the performance of traffic. This study proposes a multiagent reinforcement learning (MARL) based traffic control strategy, in which each intersection in a macroscopic fundamental diagram (MFD) region was controlled by one agent using the level of services (LOS) and MFD-based parameters as rewards. The proposed MARL strategy was evaluated by simulation in a 3×3 grid network compared with pretimed, actuated, and MFD-based traffic control strategies. The evaluation results showed that, at different demand levels, the proposed MARL strategy outperforms the other three traffic control strategies in terms of average intersection queue length and average intersection waiting time to a different extent. Results also showed that the proposed MARL dissipated the congestion faster than the other three control strategies. Results of the Friedman test indicated that the differences in performances between the proposed MARL and other strategies were statistically significant regardless of the demand level. The MFD in the testbed network controlled by the proposed MARL was different from that controlled by the pretimed strategy, especially the MFD scatter plot. It provides insights on considering the traffic flow theory of the network when applying MARL to traffic control strategies.http://dx.doi.org/10.1155/2022/5681234 |
| spellingShingle | Lingyu Zheng Bing Wu A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region Journal of Advanced Transportation |
| title | A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region |
| title_full | A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region |
| title_fullStr | A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region |
| title_full_unstemmed | A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region |
| title_short | A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region |
| title_sort | reinforcement learning based traffic control strategy in a macroscopic fundamental diagram region |
| url | http://dx.doi.org/10.1155/2022/5681234 |
| work_keys_str_mv | AT lingyuzheng areinforcementlearningbasedtrafficcontrolstrategyinamacroscopicfundamentaldiagramregion AT bingwu areinforcementlearningbasedtrafficcontrolstrategyinamacroscopicfundamentaldiagramregion AT lingyuzheng reinforcementlearningbasedtrafficcontrolstrategyinamacroscopicfundamentaldiagramregion AT bingwu reinforcementlearningbasedtrafficcontrolstrategyinamacroscopicfundamentaldiagramregion |