{"updated":"2025-01-19T07:55:41.826062+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240596","sets":["1164:4061:11479:11787"]},"path":["11787"],"owner":"44499","recid":"240596","title":["時系列滞在頻度傾向と移動傾向を組み合わせた移動先予測手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-11-11"},"_buckets":{"deposit":"472fb36e-3a5a-4940-9b4d-ceee6059f3ef"},"_deposit":{"id":"240596","pid":{"type":"depid","value":"240596","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"時系列滞在頻度傾向と移動傾向を組み合わせた移動先予測手法","author_link":["660379","660374","660380","660381","660386","660376","660375","660377","660389","660390","660382","660383","660387","660391","660385","660384","660388","660378"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"時系列滞在頻度傾向と移動傾向を組み合わせた移動先予測手法"},{"subitem_title":"Next Location Prediction Method Combining Time-Series Stay Frequency Tendency and Movement 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晋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浦野, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"米澤, 拓郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"河口, 信夫"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Haru, Terashima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shun, Takagi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoki, Tamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuyuki, Shoji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tahera, Hossain","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shin, Katayama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenta, 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report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8698","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"モバイル端末の普及に伴い，都市における大規模な位置情報データの収集が容易となり，位置情報データに基づく移動履歴を活用した研究が盛んに行われている．移動履歴から捉えられる移動傾向は，ユーザの将来の移動を予測する手掛かりとなり，予測結果はマーケティング戦略等での活用が期待できる．移動予測の分野においては，都市における大規模なユーザの移動履歴を用いて深層学習モデルを訓練し，予測を行う研究が多くなされている．しかし，ユーザが多い大規模な都市では学習可能な移動履歴が豊富にある一方で，ユーザが少ない小規模な都市では不十分で，予測精度が低くなる懸念がある．また，異なる都市間で移動履歴の共有を試みた場合，都市空間の構造が異なるため，移動履歴中の滞在エリア同士の対応付けが必要となる．そこで本研究では，異なる都市間で共通する時系列滞在頻度傾向と，各都市で固有の移動傾向を組み合わせた移動先予測手法を提案する．時系列滞在頻度とは，ユーザの移動履歴中の各滞在場所に，時間帯毎の滞在頻度を与えたもので，都市の機能的な特徴を用いずに異なる都市間で共通に扱える特徴量である．これにより，異なる都市のユーザの移動履歴を同様に扱うことが可能になる．評価実験では，都市間で共通する時系列滞在頻度傾向を学習させたモデル (SFT-BERT) および各都市で固有の移動傾向を学習させたモデル (CMT-BERT) をエンコーダとして用い，下流モデルにより移動予測を行った．その結果，単一の都市の移動履歴のみで学習および予測を行った場合に対する優位性を示した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ユビキタスコンピューティングシステム（UBI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-11-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"21","bibliographicVolumeNumber":"2024-UBI-84"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:44:52.786183+00:00","id":240596,"links":{}}