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Traffic light optimization (TLO) using reinforcement learning for automated transport systems
https://tokushima-u.repo.nii.ac.jp/records/2013461
https://tokushima-u.repo.nii.ac.jp/records/2013461fc4f2997-4021-4b94-92fc-aca92dda0734
| 名前 / ファイル | ライセンス | アクション |
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ijain_11_1_102.pdf (809 KB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||
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| 公開日 | 2025-08-20 | |||||||||||||||||||
| アクセス権 | ||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||
| 関連識別子 | https://doi.org/10.26555/ijain.v11i1.1655 | |||||||||||||||||||
| 関連名称 | 10.26555/ijain.v11i1.1655 | |||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||
| タイトル | ||||||||||||||||||||
| タイトル | Traffic light optimization (TLO) using reinforcement learning for automated transport systems | |||||||||||||||||||
| 著者 |
Hassan, Mohammad Mehedi
× Hassan, Mohammad Mehedi
× カルンガル, スティフィン ギディンシ
WEKO
1240
× 寺田, 賢治
WEKO
106
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| 抄録 | ||||||||||||||||||||
| 内容記述 | Current traffic light systems follow predefined timing sequences, causing the light to turn green even when no cars are waiting, while the side road with waiting vehicles may still face a red light. Reinforcement learning can help by training an intelligent model to analyze real-time traffic conditions and dynamically adjust signal lights based on actual demand and necessity. If the traffic light becomes intelligent and autonomous then it can significantly reduce the time wasted everyday commuting due to previously determined traffic light timing sequences. In our previous work, we used fuzzy logic to control the traffic light where the time was fixed but in this paper, the waiting time becomes a variable that changes depending on other road variables like vehicles, pedestrians, and times. Moreover, we trained an agent in this work using reinforcement learning to optimize the traffic flow in junctions with traffic lights. The trained agent worked using the greedy method to improve traffic flow to maximize the rewards by changing the signals appropriately. We have two states and there are only two actions to take for the agent. The results of the training of the model are promising. In normal situations, the average waiting time was 9.16 seconds. After applying our fuzzy rules, the average waiting time was reduced to 0.26 seconds, and after applying reinforcement learning, it was 0.12 seconds in a simulator. The average waiting time was reduced by 97~98%. These models have the potential to improve real-world traffic efficiency by approximately 67~68%. | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | Intelligent transportation systems | |||||||||||||||||||
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| 主題 | Machine learning | |||||||||||||||||||
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| 主題 | Reinforcement learning | |||||||||||||||||||
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| 主題 | Traffic management | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 主題 | Simulation and modeling | |||||||||||||||||||
| 書誌情報 |
en : International Journal of Advances in Intelligent Informatics 巻 11, 号 1, p. 102-119, 発行日 2025-02-28 |
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| 収録物識別子タイプ | PISSN | |||||||||||||||||||
| 収録物識別子 | 24426571 | |||||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||
| 収録物識別子 | 25483161 | |||||||||||||||||||
| 出版者 | ||||||||||||||||||||
| 出版者 | Universitas Ahmad Dahlan | |||||||||||||||||||
| 権利情報 | ||||||||||||||||||||
| 権利情報 | This is an open access article under the CC–BY-SA license. | |||||||||||||||||||
| EID | ||||||||||||||||||||
| 識別子 | 416951 | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
