| アイテムタイプ |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2020-12-08 |
| 書誌情報 |
en : IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
巻 7,
号 1,
p. 127-138,
発行日 2020
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| タイトル |
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|
タイトル |
Battery Maintenance of Pedelec Sharing System: Big Data Based Usage Prediction and Replenishment Scheduling |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
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主題Scheme |
Other |
|
主題 |
Intelligent transportation systems |
| キーワード |
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言語 |
en |
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主題Scheme |
Other |
|
主題 |
Big data analytics |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Artificial intelligence |
| 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 |
| アクセス権 |
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|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
チョウ, ジョウ ホウ
董, 冕雄
LUAN, Tom H.
太田, 香
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| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 抄録 |
|
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内容記述タイプ |
Abstract |
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内容記述 |
Pedelecs are an alternative of traditional share bikes by applying the battery-powered motor to assist pedaling and accordingly extend the riding coverage. The large scale deployment of pedelecs, however, requires a careful design of maintenance system to replace the batteries regularly that can be costly. This paper investigates the maintenance of a city-wide pedelec system by developing an offline solution in two steps. First, we develop an optimal and efficient hybrid prediction model which predicts the usage demand of pedelecs in every 48 h on a scale of millions of pedelecs. Our proposal predicts the future usage increment of pedelecs by combining a local predictor, a global predictor, and an inflection predictor, which captures both the short-term and long-term factors affecting the pedelec usage. Second, based on the developed predictor and results of big data analytics, an optimal path planning scheme for the replenishment of pedelec batteries is developed. As compared to other schemes, our scheme can save 40% of the maintenance cost. To verify our proposal, extensive real-data driven simulations are performed which show that the accuracy of the prediction process is high enough than each traditional method and our proposal solves the maintenance problem efficiently. |
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言語 |
en |
| 出版者 |
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出版者 |
IEEE COMPUTER SOC |
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言語 |
en |
| 出版者版へのリンク |
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表示名 |
10.1109/TNSE.2019.2901833 |
|
URL |
https://doi.org/10.1109/TNSE.2019.2901833 |
| DOI |
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関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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関連識別子 |
10.1109/TNSE.2019.2901833 |
| 日本十進分類法 |
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|
主題Scheme |
NDC |
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主題 |
007 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2327-4697 |
| 権利 |
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|
権利情報 |
© 2020 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. |
|
言語 |
en |
| 著者版フラグ |
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出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |
IEEETNSE_7_1_127_138 (4.5 MB)
