Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning
Traffic-signal control is key to ensuring smooth traffic flows in urban areas. However, controlling traffic by considering various traffic characteristics is a complex and challenging task. Although rule-based methods are typically employed, they have limitations. In this context, deep reinforcement...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10900387/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849708041661841408 |
|---|---|
| author | Takumi Saiki Sachiyo Arai |
| author_facet | Takumi Saiki Sachiyo Arai |
| author_sort | Takumi Saiki |
| collection | DOAJ |
| description | Traffic-signal control is key to ensuring smooth traffic flows in urban areas. However, controlling traffic by considering various traffic characteristics is a complex and challenging task. Although rule-based methods are typically employed, they have limitations. In this context, deep reinforcement learning-based methods have attracted attention because they do not require environmental models and can generate control policies without human intervention. Previous studies on traffic signal control have primarily employed autonomous decentralized control using the information from each intersection and implementing coordination among intersections. However, optimizing traffic control for an entire city using only data from near the intersections is challenging. In addition, learning the primitive control rules of traffic signals using all the information across a wide area significantly increases the action dimension and further complicates learning. However, this is determined ad-hoc and does not guarantee rationality. Therefore, this paper presents a hierarchical architecture that divides global and local controls. By introducing hierarchy and applying multiobjective reinforcement learning, this study proposes switching multiple intersection-control policies corresponding to the traffic-flow ratio, which guarantees Pareto optimization of traffic signal control among intersections. |
| format | Article |
| id | doaj-art-e9af711bfa754ddea86ddce86479cdb3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e9af711bfa754ddea86ddce86479cdb32025-08-20T03:15:47ZengIEEEIEEE Access2169-35362025-01-0113366583666410.1109/ACCESS.2025.354480010900387Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement LearningTakumi Saiki0https://orcid.org/0000-0002-7344-2745Sachiyo Arai1https://orcid.org/0000-0002-8899-645XGraduate School of Engineering, Chiba University, Chiba, JapanGraduate School of Engineering, Chiba University, Chiba, JapanTraffic-signal control is key to ensuring smooth traffic flows in urban areas. However, controlling traffic by considering various traffic characteristics is a complex and challenging task. Although rule-based methods are typically employed, they have limitations. In this context, deep reinforcement learning-based methods have attracted attention because they do not require environmental models and can generate control policies without human intervention. Previous studies on traffic signal control have primarily employed autonomous decentralized control using the information from each intersection and implementing coordination among intersections. However, optimizing traffic control for an entire city using only data from near the intersections is challenging. In addition, learning the primitive control rules of traffic signals using all the information across a wide area significantly increases the action dimension and further complicates learning. However, this is determined ad-hoc and does not guarantee rationality. Therefore, this paper presents a hierarchical architecture that divides global and local controls. By introducing hierarchy and applying multiobjective reinforcement learning, this study proposes switching multiple intersection-control policies corresponding to the traffic-flow ratio, which guarantees Pareto optimization of traffic signal control among intersections.https://ieeexplore.ieee.org/document/10900387/Hierarchical reinforcement learningmultiobjective optimizationtraffic-signal control |
| spellingShingle | Takumi Saiki Sachiyo Arai Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning IEEE Access Hierarchical reinforcement learning multiobjective optimization traffic-signal control |
| title | Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning |
| title_full | Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning |
| title_fullStr | Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning |
| title_full_unstemmed | Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning |
| title_short | Coordinated Traffic-Signal Control of Wide Area Network via Hierarchical Reinforcement Learning |
| title_sort | coordinated traffic signal control of wide area network via hierarchical reinforcement learning |
| topic | Hierarchical reinforcement learning multiobjective optimization traffic-signal control |
| url | https://ieeexplore.ieee.org/document/10900387/ |
| work_keys_str_mv | AT takumisaiki coordinatedtrafficsignalcontrolofwideareanetworkviahierarchicalreinforcementlearning AT sachiyoarai coordinatedtrafficsignalcontrolofwideareanetworkviahierarchicalreinforcementlearning |