{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215828","sets":["581:10784:10785"]},"path":["10785"],"owner":"44499","recid":"215828","title":["人口統計データを用いた高需要時の飲食店需要予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-01-15"},"_buckets":{"deposit":"d16b03a6-db27-4f3e-8bdf-fc7f26100398"},"_deposit":{"id":"215828","pid":{"type":"depid","value":"215828","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"人口統計データを用いた高需要時の飲食店需要予測","author_link":["556036","556038","556039","556030","556029","556037","556040","556032","556041","556033","556034","556031","556042","556035"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"人口統計データを用いた高需要時の飲食店需要予測"},{"subitem_title":"Prediction of Restaurant Sales Using Population Statistical Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:ニューノーマル時代の高度交通システムとパーベイシブシステム] 飲食店需要予測，機械学習，人口統計","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2022-01-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社ドコモ・インサイトマーケティング"},{"subitem_text_value":"株式会社NTTドコモ"},{"subitem_text_value":"株式会社サイゼリヤ"},{"subitem_text_value":"株式会社サイゼリヤ"},{"subitem_text_value":"株式会社サイゼリヤ"},{"subitem_text_value":"株式会社NTTドコモ"},{"subitem_text_value":"株式会社NTTドコモ"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"DOCOMO InsightMarketing, INC.","subitem_text_language":"en"},{"subitem_text_value":"NTT DOCOMO, INC.","subitem_text_language":"en"},{"subitem_text_value":"Saizeriya Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Saizeriya Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Saizeriya Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"NTT DOCOMO, INC.","subitem_text_language":"en"},{"subitem_text_value":"NTT DOCOMO, INC.","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/215828/files/IPSJ-JNL6301018.pdf","label":"IPSJ-JNL6301018.pdf"},"date":[{"dateType":"Available","dateValue":"2024-01-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6301018.pdf","filesize":[{"value":"1.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"923ed79d-b98f-4e50-8fcb-ef0609e3da98","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"篠田, 謙司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山田, 将人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高梨, 元樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長谷川, 大輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坪井, 哲也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"深澤, 佑介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"木本, 勝敏"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kenji, Shinoda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Yamada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Motoki, Takanashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Hasegawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetuya, Tsuboi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Fukazawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masatoshi, Kimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"飲食店において売上などの需要を予測することは，スタッフのシフトスケジューリングや店舗の事前準備を行ううえで重要なタスクである．特に，イベント開催や天候変動を契機として通常より高い売上が発生する高需要状態を事前に予測することが求められている．本研究では，過去の売上データに加え人口統計データを用いることで，高需要状態での予測精度を改善する手法を提案する．提案手法では，売上予測のモデルと，将来需要が高くなるか低くなるかを判断するモデルの2つのモデルの結果を組み合わせて売上予測を行う．飲食店における実売上データを用いて予測精度を評価した．その結果，従来手法と比較して，高需要時の需要予測精度が1.45%向上することを示した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Predicting the future sales volume is a critical task in operating a restaurant as it helps to determine the number of staff and ingredients to have at any time and decide when to start preparing food. Future sales can typically be predicted according to the demand cycle; however, it is difficult to predict an immediate increase in demand because it is out of the demand cycle. To tackle this issue, this study proposes a method for predicting the next-hour future sales volume based on population statistics data, in addition to historical sales and current and historical weather data. The proposed method combines the results of two models, one predicting the sales volume, while the other determining whether the future demand will become high or low. The proposed method was evaluated using actual restaurant data collected in collaboration with a major Japanese restaurant company. The results demonstrate that the prediction accuracy can be improved by 1.45% compared to the prediction model of sales volume without combination of demand classification when the sales volume is high.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"151","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"143","bibliographicIssueDates":{"bibliographicIssueDate":"2022-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"63"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00215720","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":215828,"updated":"2025-01-19T15:55:29.901859+00:00","links":{},"created":"2025-01-19T01:16:34.152018+00:00"}