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Bus Ridership Prediction with Time Section, Weather, and Ridership Trend Aware Multiple LSTM
http://hdl.handle.net/10061/0002000472
http://hdl.handle.net/10061/0002000472bf2ceb19-071e-49e1-a4fc-f6cb2de8e147
| 名前 / ファイル | ライセンス | アクション |
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| アイテムタイプ | 会議発表論文 / Conference Paper(1) | |||||||
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| 公開日 | 2024-06-14 | |||||||
| タイトル | ||||||||
| タイトル | Bus Ridership Prediction with Time Section, Weather, and Ridership Trend Aware Multiple LSTM | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Pervasive computing | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Precipitation | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Correlation | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Conferences | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Urban areas | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Prediction methods | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Computer architecture | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Deep Learning | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | LSTM | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Intelligent Transport Systems | |||||||
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| 主題Scheme | Other | |||||||
| 主題 | Bus Ridership Prediction | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | conference paper | |||||||
| 著者 |
Yamamura, Tatsuya
× Yamamura, Tatsuya
× 新井, イスマイル× 垣内, 正年× 遠藤, 新× 藤川, 和利 |
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Public transportation has been essential in people's lives in recent years. Bus ridership is a factor in people's choice to board the bus. Therefore, from the perspective of improving service quality, it is important to inform passengers who have not boarded the bus yet about future bus ridership. However, there is a concern that providing inaccurate information may cause a negative experience. Against this backdrop, there is a need to provide bus passengers who have not boarded yet with highly accurate predictions. Many researchers are working on studies on this. However, two issues summarize related studies. The first is that the correlation of bus ridership between consecutive bus stops should be considered for the prediction. The second is that the prediction has yet to be made using all of the features shown to be useful in each related study. This study proposes a prediction method that addresses both of these issues. We solve the first issue by designing an LSTM-based architecture for each bus stop and a single model for the entire bus stop. We solve the second issue by inputting all useful data, the past bus ridership, day of the week, time section, weather, and precipitation, as features. Bus ridership at each bus stop collected from buses operated by Minato Kanko Bus Inc, in Kobe city, Hyogo, Japan, from October 1, 2021, to September 30, 2022, were used to compare accuracy. The proposed method improved RMSE by 23% on average and up to 27% compared to existing methods. | |||||||
| 書誌情報 |
en : 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) p. 509-514, 発行日 2023-06-21 |
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| 会議情報 | ||||||||
| 会議名 | 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) | |||||||
| 開始年 | 2023 | |||||||
| 開始月 | 03 | |||||||
| 開始日 | 13 | |||||||
| 終了年 | 2023 | |||||||
| 終了月 | 03 | |||||||
| 終了日 | 17 | |||||||
| 開催地 | Atlanta | |||||||
| 開催国 | USA | |||||||
| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1109/PerComWorkshops56833.2023.10150218 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://ieeexplore.ieee.org/abstract/document/10150218 | |||||||
| 権利 | ||||||||
| 権利情報 | $00A92023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 出版社許諾条件により、本文は2025年6月21日以降に公開 | |||||||
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| 出版タイプ | AM | |||||||
