Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54345
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dc.contributor.authorSanti Phithakkitnukoonen_US
dc.contributor.authorTeerayut Horanonten_US
dc.contributor.authorApichon Witayangkurnen_US
dc.contributor.authorRaktida Sirien_US
dc.contributor.authorYoshihide Sekimotoen_US
dc.contributor.authorRyosuke Shibasakien_US
dc.date.accessioned2018-09-04T10:12:14Z-
dc.date.available2018-09-04T10:12:14Z-
dc.date.issued2015-04-01en_US
dc.identifier.issn15741192en_US
dc.identifier.other2-s2.0-85027925252en_US
dc.identifier.other10.1016/j.pmcj.2014.07.003en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85027925252&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54345-
dc.description.abstract© 2014 Elsevier B.V. All rights reserved. This article describes a framework that capitalizes on the large-scale opportunistic mobile sensing approach for tourist behavior analysis. The article describes the use of massive mobile phone GPS location records to study tourist travel behavior, in particular, number of trips made, time spent at destinations, and mode of transportation used. Moreover, this study examined the relationship between personal mobility and tourist travel behavior and offered a number of interesting insights that are useful for tourism, such as tourist flows, top tourist destinations or origins, top destination types, top modes of transportation in terms of time spent and distance traveled, and how personal mobility information can be used to estimate the likelihood in tourist travel behavior, i.e., number of trips, time spent at destinations, and trip distance. Furthermore, the article describes an application developed based on the analysis in this study that allows the user to observe touristic, non-touristic, and commuting trips along with home and workplace locations as well as tourist flows, which can be useful for urban planners, transportation management, and tourism authorities.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleUnderstanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japanen_US
dc.typeJournalen_US
article.title.sourcetitlePervasive and Mobile Computingen_US
article.volume18en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsThammasat Universityen_US
article.stream.affiliationsUniversity of Tokyoen_US
article.stream.affiliationsMaejo Universityen_US
Appears in Collections:CMUL: Journal Articles

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