{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233923","sets":["1164:3865:11476:11605"]},"path":["11605"],"owner":"44499","recid":"233923","title":["ニューラルネットワークを用いたダイヤ乱れ時の列車乗車率予測モデルの考案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-05-08"},"_buckets":{"deposit":"aa74f4bf-ab1e-4af2-a4cf-3b322467fe21"},"_deposit":{"id":"233923","pid":{"type":"depid","value":"233923","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ニューラルネットワークを用いたダイヤ乱れ時の列車乗車率予測モデルの考案","author_link":["636491","636492","636494","636495","636488","636493","636490","636489"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークを用いたダイヤ乱れ時の列車乗車率予測モデルの考案"},{"subitem_title":"Consideration of Train Congestion Rate Prediction Model under Disruption using Neural 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Institute","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/233923/files/IPSJ-MBL24111002.pdf","label":"IPSJ-MBL24111002.pdf"},"date":[{"dateType":"Available","dateValue":"2026-05-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MBL24111002.pdf","filesize":[{"value":"786.5 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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，鉄道事業者はアプリ等を通じて旅客に列車の混雑予測情報を提供しているが，ダイヤ乱れ時は混雑傾向が平常時と異なるため，現状ではダイヤ乱れ時の利用列車変更の判断のための十分な情報提供ができていない．本研究では，ダイヤ乱れ時を対象に，各列車の各駅発時点の乗車率を精度良く予測することを目的に，ニューラルネットワークを用いた乗車率予測モデルを考案した．モデルの入出力データが異なる 4 パターンのモデルを検討し，比較評価を行った結果，出力データを予測対象列車の予測時点の駅の乗車率とそれより先の予測対象駅の乗車率の差分値としたモデルが，予測対象駅の乗車率そのものとするモデルよりも精度良く予測できることを確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","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":"2024-05-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2024-MBL-111"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233923,"updated":"2025-01-19T09:56:10.952094+00:00","links":{},"created":"2025-01-19T01:35:34.883426+00:00"}