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Learning to Control Traffic Lights for Near-Saturated Traffic Flow
http://hdl.handle.net/10445/8453
http://hdl.handle.net/10445/8453f3a2c279-eaa2-4e31-89db-96135a5acdfe
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2011-09-30-ISIS2011-Wakahara-383p.pdf (713.0 kB)
Download is available from 2999/12/31.
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-03-24 | |||||
| タイトル | ||||||
| タイトル | Learning to Control Traffic Lights for Near-Saturated Traffic Flow | |||||
| 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_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Wakahara, Takumi
× Wakahara, Takumi× 三上, 貞芳 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Traffic light control method that uses discrete dynamical systems model and feedback control approach is known as both practically and theoretically promising methodology. This is based on describing inflow and outflow at a junction, and deriving feedback gain for green light timing control. One of the key factors is a vehicle’s turning ratio at each junction. However, the value is not directly measurable in real-time, and off-line estimated values are used instead. This paper focuses on the fact that the degree of improvement of traffic flow is measurable by using a specific value is known afterward, and uses Reinforcement Learning method to search for a good turning ratio values through on-line trials. | |||||
| 書誌情報 |
12th International Symposium on Advanced Intelligent Systems p. 383-386, 発行日 2011-09-30 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 国際会議/International Conference | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
