IMPUTING SOCIAL DEMOGRAPHIC INFORMATION BASED ON PASSIVELY COLLECTED LOCATION DATA AND MACHINE LEARNING METHODS

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dc.contributor.advisorZhang, Leien_US
dc.contributor.authorPan, Yixuanen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-09-12T05:40:16Z
dc.date.available2018-09-12T05:40:16Z
dc.date.issued2018en_US
dc.description.abstractMultiple types of passively collected location data (PCLD) have emerged during the past 20 years. Its capability in travel demand analysis has also been studied and revealed. Unlike the traditional surveys whose sample is designed efficiently and carefully, PCLD features a non-probabilistic sample of dramatically larger size. However, PCLD barely contains any ground truth for both the human subjects involved and the movements they produce. The imputation for such missing information has been evaluated for years, including origin and destination, travel mode, trip purpose, etc. This research intends to advance the utilization of PCLD by imputing social demographic information, which can help to create a panorama for the large volume of travel behaviors observed and to further develop a rational weighting procedure for PCLD. The Conditional Inference Tree model has been employed to address the problems because of its abilities to avoid biased variable selection and overfitting.en_US
dc.identifierhttps://doi.org/10.13016/M2SQ8QM8P
dc.identifier.urihttp://hdl.handle.net/1903/21232
dc.language.isoenen_US
dc.subject.pqcontrolledTransportationen_US
dc.subject.pquncontrolledconditional inference treeen_US
dc.subject.pquncontrolleddemographic imputationen_US
dc.subject.pquncontrolledfeature set constructionen_US
dc.subject.pquncontrolledpassively collected location dataen_US
dc.titleIMPUTING SOCIAL DEMOGRAPHIC INFORMATION BASED ON PASSIVELY COLLECTED LOCATION DATA AND MACHINE LEARNING METHODSen_US
dc.typeThesisen_US

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