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| ■論文No. |
CMN24028 |
| ■ページ数 |
6ページ |
■発行日
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2024/03/25 |
| ■タイトル |
Merchant Relative Dwell Time Ranking for Electric Vehicle Charger Merchant Site Using Semi-Supervised Learning |
| ■タイトル(英語) |
Merchant Relative Dwell Time Ranking for Electric Vehicle Charger Merchant Site Using Semi-Supervised Learning |
| ■著者名 |
Kumar Sheetal(Hitachi India Private Limited),Kumar Sharath (Hitachi India Private Limited),Sharma Ankit(Hitachi India Private Limited),Kumar Vinoth (Hitachi India Private Limited) |
| ■著者名(英語) |
Sheetal Kumar(Hitachi India Private Limited),Sharath Kumar(Hitachi India Private Limited),Ankit Sharma (Hitachi India Private Limited),Vinoth Kumar (Hitachi India Private Limited) |
| ■価格 |
会員 ¥220 一般 ¥330 |
| ■書籍種類 |
研究会(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 通信研究会 |
| ■本誌 |
2024年3月28日-2024年3月29日通信研究会
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| ■本誌掲載ページ |
61-66ページ |
| ■原稿種別 |
英語 |
| ■電子版へのリンク |
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| ■キーワード |
Dwell time |EV Charging |site selection|Dwell time |EV Charging |site selection |
| ■要約(日本語) |
Dwell time is the amount of time spent by customer at a merchant location for making a purchase. It is required to assess customers’ behavior, shopping habits, merchant placement and to evaluate existing locations for value-added services like Electric Vehicles (EVs) charging stations. We propose a novel method to estimate dwell time at merchant locations by using payment transaction data to identify profitable locations for EV charging stations. We use a combination of supervised and unsupervised learning to train a set of classifiers and establish relative dwell time rank. The results indicate the method exhibited an accuracy of 74%. |
| ■要約(英語) |
Dwell time is the amount of time spent by customer at a merchant location for making a purchase. It is required to assess customers’ behavior, shopping habits, merchant placement and to evaluate existing locations for value-added services like Electric Vehicles (EVs) charging stations. We propose a novel method to estimate dwell time at merchant locations by using payment transaction data to identify profitable locations for EV charging stations. We use a combination of supervised and unsupervised learning to train a set of classifiers and establish relative dwell time rank. The results indicate the method exhibited an accuracy of 74%. |
| ■版 型 |
A4 |
| ■PDFファイルサイズ |
1,296Kバイト |
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