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Feedback Control of Traffic Signal Network of Less Traffic Sensors by Help of Machine Learning
http://hdl.handle.net/10445/8545
http://hdl.handle.net/10445/8545d09928e5-57cc-41e7-a595-8150b78a0fc9
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IAS2012wakahara9.pdf (480.7 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2017-05-18 | |||||
| タイトル | ||||||
| タイトル | Feedback Control of Traffic Signal Network of Less Traffic Sensors by Help of Machine Learning | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 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 | |||||
| 著者 |
Wakahara, Takumi
× Wakahara, Takumi× MIKAMI, Sadayoshi |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | As a way of resolving vehicle congestion, there is a feedback control approach which models a traffic network as a discrete dynamical system and derives feedback gain for controlling green light times of each junction. Since the input is the sensory observed traffic flow of each link, and since the state equation models both the topology and the parameters of the network, it is effective for adaptive control of a wide area traffic in real-time. One of the essential factors in a state equation is the vehicles’ turning ratio at each junction. However, in a normal traffic sensor layout, it is impossible to directly measure this value in real-time, and values from traffic census are used. This paper is to propose a method that predicts this value in real-time through machine learning and gives more appropriate feedback control. Out idea is to find the turning ratio through probabilistic search by Reinforcement Learning referring to the degree of improvement of the entire traffic flow. At this moment we have finished formulation of the scheme and the verification for the performance by a traffic simulator is on the way. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | doi: 10.1007/978-3-642-33932-5_81 | |||||
| 書誌情報 |
Intelligent Autonomous Systems 12 p. 853-861, 発行日 2013 |
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| ISBN | ||||||
| 識別子タイプ | ISBN | |||||
| 関連識別子 | 9783642339318 | |||||
| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 原著論文/Original Paper | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1007/978-3-642-33932-5_81 | |||||
| 権利 | ||||||
| 権利情報 | © Springer-Verlag Berlin Heidelberg 2013 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
| 出版者 | ||||||
| 出版者 | Springer, Berlin, Heidelberg | |||||
