Deep reinforcement learning for traffic signal control: A review
Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has sho...
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my.um.eprints.371542023-05-17T06:47:13Z http://eprints.um.edu.my/37154/ Deep reinforcement learning for traffic signal control: A review Rasheed, Faizan Yau, Kok-Lim Alvin Md. Noor, Rafidah Wu, Celimuge Low, Yeh-Ching QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC. Institute of Electrical and Electronics Engineers 2020 Article PeerReviewed Rasheed, Faizan and Yau, Kok-Lim Alvin and Md. Noor, Rafidah and Wu, Celimuge and Low, Yeh-Ching (2020) Deep reinforcement learning for traffic signal control: A review. IEEE Access, 8. pp. 208016-208044. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.3034141 <https://doi.org/10.1109/ACCESS.2020.3034141>. 10.1109/ACCESS.2020.3034141 |
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QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Rasheed, Faizan Yau, Kok-Lim Alvin Md. Noor, Rafidah Wu, Celimuge Low, Yeh-Ching Deep reinforcement learning for traffic signal control: A review |
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Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC. |
format |
Article |
author |
Rasheed, Faizan Yau, Kok-Lim Alvin Md. Noor, Rafidah Wu, Celimuge Low, Yeh-Ching |
author_facet |
Rasheed, Faizan Yau, Kok-Lim Alvin Md. Noor, Rafidah Wu, Celimuge Low, Yeh-Ching |
author_sort |
Rasheed, Faizan |
title |
Deep reinforcement learning for traffic signal control: A review |
title_short |
Deep reinforcement learning for traffic signal control: A review |
title_full |
Deep reinforcement learning for traffic signal control: A review |
title_fullStr |
Deep reinforcement learning for traffic signal control: A review |
title_full_unstemmed |
Deep reinforcement learning for traffic signal control: A review |
title_sort |
deep reinforcement learning for traffic signal control: a review |
publisher |
Institute of Electrical and Electronics Engineers |
publishDate |
2020 |
url |
http://eprints.um.edu.my/37154/ |
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1768007312180510720 |