2024-03-29T22:26:03Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001474232022-10-21T05:24:51Z00581:08417:08418
Trip-Extraction Method Based on Characteristics of Sensors and Human-Travel Behavior for Sensor-Based Travel SurveyTrip-Extraction Method Based on Characteristics of Sensors and Human-Travel Behavior for Sensor-Based Travel Surveyeng[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)http://id.nii.ac.jp/1001/00147389/Journal Articlehttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=147423&item_no=1&attribute_id=1&file_no=1Copyright (c) 2016 by the Information Processing Society of JapanHitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems Engineering/Presently with Hitachi Europe Ltd.Hitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems EngineeringHitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems EngineeringHitachi, Ltd., Research & Development Group, Center for Technology Innovation - Systems EngineeringHitachi Asia Ltd.Hiroki, OhashiPhong, Xuan,NguyenTakayuki, AkiyamaMasaaki, YamamotoAkiko, SatoA 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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online)DOI http://dx.doi.org/10.2197/ipsjjip.24.39------------------------------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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.24(2016) No.1 (online)DOI http://dx.doi.org/10.2197/ipsjjip.24.39------------------------------AN00116647情報処理学会論文誌5712016-01-151882-77642016-01-13