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Improving Bus Arrival Time Prediction Accuracy with Daily Periodic Based Transportation Data Imputation
http://hdl.handle.net/10061/0002000223
http://hdl.handle.net/10061/0002000223b47b0ee9-6aee-4a62-94cd-390e2ad6042f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
fulltext (72 KB)
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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 | 2024-05-16 | |||||||
| タイトル | ||||||||
| タイトル | Improving Bus Arrival Time Prediction Accuracy with Daily Periodic Based Transportation Data Imputation | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | conference paper | |||||||
| 著者 |
Niwa, Takumi
× Niwa, Takumi
× 新井, イスマイル× 遠藤, 新× 垣内, 正年× 藤川, 和利 |
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Providing a predicted bus arrival time (BAT) allows bus users to choose a route with less wait time. The bus operation data needed to predict BAT is sometimes missing and must be imputed. However, the existing studies use simple methods such as last observation carried forward to impute missing bus operation data. On the other hand, in studies on traffic congestion prediction, it was reported that the prediction error was reduced by using an imputation method focusing on data characteristics. In this research, we aim to reduce the prediction error of BAT prediction by using an imputation method that focuses on the characteristics of bus operation data. We propose a pattern imputation based on the daily periodicity of bus operation data. We compared the proposed methods with a simple imputation method for BAT prediction. As a result of the experiment, we found that pattern imputation is the most effective method for imputation for multiple trips BAT prediction. | |||||||
| 書誌情報 |
en : 2023 IEEE International Conference on Smart Mobility (SM) p. 126-131, 発行日 2023-05-03 |
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| 会議情報 | ||||||||
| 会議名 | 2023 IEEE International Conference on Smart Mobility (SM) | |||||||
| 開始年 | 2023 | |||||||
| 開始月 | 03 | |||||||
| 開始日 | 19 | |||||||
| 終了年 | 2023 | |||||||
| 終了月 | 03 | |||||||
| 終了日 | 21 | |||||||
| 開催地 | Thuwal | |||||||
| 開催国 | SAU | |||||||
| 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/SM57895.2023.10112252 | |||||||
| 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/document/10112252 | |||||||
| 権利 | ||||||||
| 権利情報 | $00A9 2023 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5月3日以降に公開 | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | AM | |||||||
