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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.17303 |
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| _version_ | 1866914885322932224 |
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| author | Shao, Junqi Zheng, Chenhao Chen, Yuxuan Huang, Yucheng Zhang, Rui |
| author_facet | Shao, Junqi Zheng, Chenhao Chen, Yuxuan Huang, Yucheng Zhang, Rui |
| contents | This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods. It employs a lane-level control approach using the FRAP algorithm to achieve dynamic and adaptive traffic signal control, optimizing traffic flow, reducing congestion, and improving overall efficiency. Our research demonstrates the scalability and effectiveness of MoveLight across single intersections, arterial roads, and network levels. Experimental results using real-world datasets from Cologne and Hangzhou show significant improvements in metrics such as queue length, delay, and throughput compared to existing methods. This study highlights the transformative potential of deep reinforcement learning in intelligent traffic signal control, setting a new standard for sustainable and efficient urban transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning Shao, Junqi Zheng, Chenhao Chen, Yuxuan Huang, Yucheng Zhang, Rui Machine Learning This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods. It employs a lane-level control approach using the FRAP algorithm to achieve dynamic and adaptive traffic signal control, optimizing traffic flow, reducing congestion, and improving overall efficiency. Our research demonstrates the scalability and effectiveness of MoveLight across single intersections, arterial roads, and network levels. Experimental results using real-world datasets from Cologne and Hangzhou show significant improvements in metrics such as queue length, delay, and throughput compared to existing methods. This study highlights the transformative potential of deep reinforcement learning in intelligent traffic signal control, setting a new standard for sustainable and efficient urban transportation systems. |
| title | MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.17303 |