{"created":"2025-01-19T01:12:28.140364+00:00","updated":"2025-01-19T17:51:11.277865+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00211284","sets":["1164:3865:10488:10574"]},"path":["10574"],"owner":"44499","recid":"211284","title":["注意機構を用いたGraph Convolutional Networksによる短期的将来滞在人口数推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-05-20"},"_buckets":{"deposit":"4f2d7275-4a60-4d06-85f2-770892a70bf4"},"_deposit":{"id":"211284","pid":{"type":"depid","value":"211284","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"注意機構を用いたGraph Convolutional Networksによる短期的将来滞在人口数推定","author_link":["536527","536525","536526"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"注意機構を用いたGraph Convolutional Networksによる短期的将来滞在人口数推定"}]},"item_type_id":"4","publish_date":"2021-05-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京都立大学都市環境学部観光科学科／現在，東京工業大学情報理工学院情報工学系"},{"subitem_text_value":"東京都立大学都市環境学部観光科学科"},{"subitem_text_value":"東京都立大学都市環境学部観光科学科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Tourism Science, Faculty of Urban Environmental Sciences, Tokyo Metropolitan University / Presently with Department of Computer Science, School of Engineering, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Tourism Science, Faculty of Urban Environmental Sciences, Tokyo Metropolitan University","subitem_text_language":"en"},{"subitem_text_value":"Department of Tourism Science, Faculty of Urban Environmental Sciences, Tokyo Metropolitan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"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/211284/files/IPSJ-MBL21099004.pdf","label":"IPSJ-MBL21099004.pdf"},"date":[{"dateType":"Available","dateValue":"2023-05-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MBL21099004.pdf","filesize":[{"value":"2.6 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":"35"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"93d2ca50-a394-4446-a2af-2d0d1286fdfe","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"久保田, 祐輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"清水, 哲夫"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大平, 悠季"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11851388","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8817","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年位置測位技術の発達により，都市において多種多様なデータが観測・収集されており，それらの利活用に対する関心が高まっている．特に短期的な将来における滞在人口数の推定は，混雑緩和を始めとする様々な都市政策の施行において重要となる．都市空間で観測される時系列データには観測地点の位置情報が付与されるため，複雑な時空間構造が生じることで知られている．既存研究の多くは観測地域をグリッドに分割し，畳み込みニューラルネットワーク（CNN）を適用することで，空間的相関の把握を試みている．しかし，CNN は空間構造を幾何学的に処理しているに過ぎず，現実の都市に関連する情報を考慮できていない．そこで，本研究では都市に偏在する情報に基づき複数のグラフを作成し，Graph Convolutional Networks（GCNs）と注意機構を使用することで，都市特有の空間的相関を多面的に把握する予測手法 Attention based Contextual Multi-View Graph Convolutiona Networks（ACMV-GCNs）を提案する．注意機構に基づき天候や時刻といった外的要因を考慮することで，従来手法よりも現実の状況に則した予測を行うことが可能となる．携帯端末より取得された人口統計データを使用し，将来滞在人口数の推定精度の検証を行うことで，提案手法が既存手法と比較して優れた推定精度を誇ることを示す．加えて，注意機構によって計算された重みを可視化することで，提案手法が予測時の祝祭日情報と時刻情報に基づき，各グラフを用いて計算された出力値を効果的に活用していることを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告モバイルコンピューティングと新社会システム（MBL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-05-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2021-MBL-99"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":211284,"links":{}}