{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240169","sets":["6164:6165:6640:11802"]},"path":["11802"],"owner":"44499","recid":"240169","title":["モバイルデータを用いた鉄道利用者数の推定と混雑予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-19"},"_buckets":{"deposit":"166af84d-3d0a-46ab-b48c-37ec9bda4104"},"_deposit":{"id":"240169","pid":{"type":"depid","value":"240169","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"モバイルデータを用いた鉄道利用者数の推定と混雑予測","author_link":["658531","658532","658533"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"モバイルデータを用いた鉄道利用者数の推定と混雑予測"},{"subitem_title":"Estimation of Railway Passengers and Congestion Prediction Using Mobile Data","subitem_title_language":"en"}]},"item_type_id":"18","publish_date":"2024-06-19","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社NTTドコモ"},{"subitem_text_value":"株式会社NTTドコモ"},{"subitem_text_value":"株式会社NTTドコモ"}]},"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/240169/files/IPSJ-DICOMO2024053.pdf","label":"IPSJ-DICOMO2024053.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2024053.pdf","filesize":[{"value":"1.7 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":"44"}],"accessrole":"open_date","version_id":"0dfbebb3-d0e4-4a67-b305-eec51eb7d390","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石黒, 慎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 喬"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"石川, 太朗"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，モバイルネットワークの位置情報データを活用し，鉄道利用者数の推定と将来の混雑予測を行う新しい手法を提案している．まず基地局の位置情報からユーザーの鉄道利用 (乗車駅・降車駅)を推定する．次にイベント開催時の混雑を考慮するため，人口統計データをクラスタリングしてイベント参加者数を抽出する．さらに人口統計，気象予報，イベント情報などの外部データを説明変数として，機械学習モデル(LightGBM)を用いて将来の各駅の入退場者数を予測する．実データによる実験では，鉄道施設内に新規にセンサーを敷設することなく予測できるようになるメリットが示された．モバイルデータを活用することで，駅施設外の混雑情報も取り入れられ，精度向上が見込まれる．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"409","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2024論文集"}],"bibliographicPageStart":"401","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-19","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T08:03:41.516496+00:00","created":"2025-01-19T01:44:12.500962+00:00","id":240169}