Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations
Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/5/114 |
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| author | Panagiotis Michailidis Iakovos Michailidis Charalampos Rafail Lazaridis Elias Kosmatopoulos |
| author_facet | Panagiotis Michailidis Iakovos Michailidis Charalampos Rafail Lazaridis Elias Kosmatopoulos |
| author_sort | Panagiotis Michailidis |
| collection | DOAJ |
| description | Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. In response, adaptive signal control—powered predominantly by reinforcement learning—has emerged as a promising data-driven solution for optimizing signal operations in evolving traffic environments. The current review presents a comprehensive analysis of high-impact reinforcement-learning-based traffic signal control methods, evaluating their contributions across numerous key dimensions: methodology type, multi-agent architectures, reward design, performance evaluation, baseline comparison, network scale, practical applicability, and simulation platforms. Through a systematic examination of the most influential studies, the review identifies dominant trends, unresolved challenges, and strategic directions for future research. The findings underscore the transformative potential of RL in enabling intelligent, responsive, and sustainable traffic management systems, marking a significant shift toward next-generation urban mobility solutions. |
| format | Article |
| id | doaj-art-04cf2127e1ef4bdbb2dd3899d5e07215 |
| institution | OA Journals |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-04cf2127e1ef4bdbb2dd3899d5e072152025-08-20T01:56:31ZengMDPI AGInfrastructures2412-38112025-05-0110511410.3390/infrastructures10050114Traffic Signal Control via Reinforcement Learning: A Review on Applications and InnovationsPanagiotis Michailidis0Iakovos Michailidis1Charalampos Rafail Lazaridis2Elias Kosmatopoulos3Center for Research and Technology Hellas, 57001 Thessaloniki, GreeceCenter for Research and Technology Hellas, 57001 Thessaloniki, GreeceCenter for Research and Technology Hellas, 57001 Thessaloniki, GreeceCenter for Research and Technology Hellas, 57001 Thessaloniki, GreeceTraffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. In response, adaptive signal control—powered predominantly by reinforcement learning—has emerged as a promising data-driven solution for optimizing signal operations in evolving traffic environments. The current review presents a comprehensive analysis of high-impact reinforcement-learning-based traffic signal control methods, evaluating their contributions across numerous key dimensions: methodology type, multi-agent architectures, reward design, performance evaluation, baseline comparison, network scale, practical applicability, and simulation platforms. Through a systematic examination of the most influential studies, the review identifies dominant trends, unresolved challenges, and strategic directions for future research. The findings underscore the transformative potential of RL in enabling intelligent, responsive, and sustainable traffic management systems, marking a significant shift toward next-generation urban mobility solutions.https://www.mdpi.com/2412-3811/10/5/114reinforcement learningtraffic managementtraffic signal controladaptive controlmodel-free controlintelligent transportation |
| spellingShingle | Panagiotis Michailidis Iakovos Michailidis Charalampos Rafail Lazaridis Elias Kosmatopoulos Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations Infrastructures reinforcement learning traffic management traffic signal control adaptive control model-free control intelligent transportation |
| title | Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations |
| title_full | Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations |
| title_fullStr | Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations |
| title_full_unstemmed | Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations |
| title_short | Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations |
| title_sort | traffic signal control via reinforcement learning a review on applications and innovations |
| topic | reinforcement learning traffic management traffic signal control adaptive control model-free control intelligent transportation |
| url | https://www.mdpi.com/2412-3811/10/5/114 |
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