{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00079975","sets":["581:6644:6645"]},"path":["6645"],"owner":"11","recid":"79975","title":["混合自己回帰隠れマルコフモデルによる歩行者行き先予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-01-15"},"_buckets":{"deposit":"b817c3de-13d2-4134-a972-ef3be94eab46"},"_deposit":{"id":"79975","pid":{"type":"depid","value":"79975","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"混合自己回帰隠れマルコフモデルによる歩行者行き先予測","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"混合自己回帰隠れマルコフモデルによる歩行者行き先予測"},{"subitem_title":"Autoregressive Hidden Markov Model for Pedestrian-movements Prediction","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特集：新たな展開を迎えるITS、モバイル通信とユビキタスコンピューティング","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2012-01-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日立製作所"},{"subitem_text_value":"日立製作所"},{"subitem_text_value":"日立製作所"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Hitachi, Ltd., Central Research Laboratory","subitem_text_language":"en"},{"subitem_text_value":"Hitachi, Ltd., Central Research Laboratory","subitem_text_language":"en"},{"subitem_text_value":"Hitachi, Ltd., Central Research Laboratory","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/79975/files/IPSJ-JNL5301038.pdf"},"date":[{"dateType":"Available","dateValue":"2014-01-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5301038.pdf","filesize":[{"value":"1.2 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5285e105-8781-4720-92fe-de18977cd412","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"浅原, 彰規"},{"creatorName":"佐藤, 暁子"},{"creatorName":"丸山, 貴志子"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Akinori, Asahara","creatorNameLang":"en"},{"creatorName":"Akiko, Sato","creatorNameLang":"en"},{"creatorName":"Kishiko, Maruyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，ある領域を訪れた多数の過去の歩行者の測位データから新たな歩行者の行き先を予測する方式として，混合自己回帰隠れマルコフモデルを用いた方式を提案する．本方式は従来の混合マルコフモデルに基づく方式に歩行者の内部状態の時間変化を加味した方式である．提案方式の評価のため，商業施設来店者の測位データに提案方式を適用し，来店者が次に行く地点を予測する実験を行った．その結果，従来70%程度であった予測精度が，提案方式では最大で80%以上になることを確認した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In order to predict a visitor's movements by past visitors' trajectories, a method using Autoregressive hidden Markov model is proposed in this research. The proposed method is an improved conventional method to take into account temporal transition of pedestrians' internal states. To evaluate the method, an experiment was performed by using actual tracking-data of visitors in a shopping mall. As the result, a prediction rate, which marked 70% by the conventional method, is improved to 80% in maximum by the proposed method.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"351","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"342","bibliographicIssueDates":{"bibliographicIssueDate":"2012-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"53"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:34:33.458419+00:00","updated":"2025-01-21T20:01:27.242437+00:00","id":79975}