Trip-Extraction Method Based on Characteristics of Sensors and Human-Travel Behavior for Sensor-Based Travel Survey

• Item type: Journal(1)
• 公開日: 2016-01-15
• タイトル: Trip-Extraction Method Based on Characteristics of Sensors and Human-Travel Behavior for Sensor-Based Travel Survey
• タイトル / 言語: en
• タイトル: Trip-Extraction Method Based on Characteristics of Sensors and Human-Travel Behavior for Sensor-Based Travel Survey
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：スマートコミュニティ実現のための高度交通システムとモバイル通信] intelligent transport systems (ITS), travel behavior survey, smartphone, machine learning, hidden Markov model (HMM)
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering／Presently with Hitachi Europe Ltd.
• 著者所属: Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属: Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属: Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属: Hitachi Asia Ltd.
• 著者所属(英): en
• 著者所属(英): Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering / Presently with Hitachi Europe Ltd.
• 著者所属(英): en
• 著者所属(英): Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属(英): en
• 著者所属(英): Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属(英): en
• 著者所属(英): Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering
• 著者所属(英): en
• 著者所属(英): Hitachi Asia Ltd.
• 著者名: Hiroki, Ohashi; Phong, Xuan,Nguyen; Takayuki, Akiyama; Masaaki, Yamamoto; Akiko, Sato
• 著者名(英): Hiroki, Ohashi; Phong, Xuan Nguyen; Takayuki, Akiyama; Masaaki, Yamamoto; Akiko, Sato
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A novel method for extracting “trip periods,” i.e., periods in which a person travels, from continuously collected sensor data, called a “trip-extraction method” hereafter, is proposed to make a sensor-based travel-behavior survey possible. There are mainly two drawbacks in previous studies that detect “stay periods,” i.e., periods in which a person stays within an area, by using the boundary of a “stay area,” i.e., an area in which a person stays and then regard the rest of the periods as trip periods: false positives caused by GPS-positioning errors and false negatives caused by short-distance trips within the boundary. This study solves these problems by using novel features that are effective even in the case where the GPS-positioning error is large and by classifying every single piece of GPS data into either trip periods or stay periods not on the basis of the stay-area boundary but on the newly proposed features. An experimental evaluation showed that the precision of the proposed method was 89.4%, which is much higher than that of conventional methods.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.39; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A novel method for extracting “trip periods,” i.e., periods in which a person travels, from continuously collected sensor data, called a “trip-extraction method” hereafter, is proposed to make a sensor-based travel-behavior survey possible. There are mainly two drawbacks in previous studies that detect “stay periods,” i.e., periods in which a person stays within an area, by using the boundary of a “stay area,” i.e., an area in which a person stays and then regard the rest of the periods as trip periods: false positives caused by GPS-positioning errors and false negatives caused by short-distance trips within the boundary. This study solves these problems by using novel features that are effective even in the case where the GPS-positioning error is large and by classifying every single piece of GPS data into either trip periods or stay periods not on the basis of the stay-area boundary but on the newly proposed features. An experimental evaluation showed that the precision of the proposed method was 89.4%, which is much higher than that of conventional methods.; \n------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.24.39; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 57, 号 1, 発行日 2016-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764