{"links":{},"id":229963,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229963","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"229963","title":["E(2)-同変グラフニューラルネットワークによる人流予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"ea6c164f-bb0f-4750-9c09-0d4328375f7b"},"_deposit":{"id":"229963","pid":{"type":"depid","value":"229963","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"E(2)-同変グラフニューラルネットワークによる人流予測","author_link":["618660","618658","618659","618661"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"E(2)-同変グラフニューラルネットワークによる人流予測"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大"},{"subitem_text_value":"RICOS"},{"subitem_text_value":"産総研"},{"subitem_text_value":"京大"}]},"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/229963/files/IPSJ-Z85-4R-08.pdf","label":"IPSJ-Z85-4R-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-4R-08.pdf","filesize":[{"value":"284.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"83d048fd-c4a1-408b-8d09-5119b3409f4c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"川上, 健太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"堀江, 正信"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大西, 正輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹内, 孝"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"理学や工学などの分野では,対象の振る舞いの理解を目的としたシミュレーションが盛んに研究されている.中でも都市空間における人流予測は,災害時の迅速な避難の誘導に有用とされている.従来,人流予測ではSocial Force Modelに代表されるモデル駆動型シミュレータが主に使用されてきた.一方,データ駆動型の手法であるグラフニューラルネットワーク(GNN)による人流予測の精度向上が注目されている.しかし,GNNが物理的な制約を考慮しないために生じる過学習が問題となっている.我々は人流予測GNNが満たすべき性質として同変性に着目し,同変性を備えたGNNにより人流予測の精度を高めることに成功した.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"332","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"331","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:29:26.302299+00:00","updated":"2025-01-19T11:20:43.333344+00:00"}