Multimodal Travel Mode Imputation based on Passively Collected Mobile Device Location Data

Simple item page
dc.contributor.advisorZhang, Leien_US
dc.contributor.authorYang, Mofengen_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.accessioned2020-07-14T05:32:49Z
dc.date.available2020-07-14T05:32:49Z
dc.date.issued2020en_US
dc.description.abstractPassively collected mobile device location (PCMDL) data contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, PCMDL data have larger spatial, temporal and population coverage while lack of ground truth information. This study proposes a framework to identify trip ends and impute travel modes from the PCMDL data. The proposed framework firstly identify trip ends using the Spatio-temporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning methods. A PCMDL dataset with ground truth information is used to calibrate and validate the proposed framework, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes using the Random Forest (RF) classifier. The proposed framework is then applied to two large-scale PCMDL datasets, covering Maryland and the entire U.S. The mode share results are compared against travel surveys at different geographic levels.en_US
dc.identifierhttps://doi.org/10.13016/hhqb-jnk0
dc.identifier.urihttp://hdl.handle.net/1903/26291
dc.language.isoenen_US
dc.subject.pqcontrolledTransportationen_US
dc.subject.pquncontrolledMobile Device Location Dataen_US
dc.subject.pquncontrolledMode Shareen_US
dc.subject.pquncontrolledTravel Behavioren_US
dc.titleMultimodal Travel Mode Imputation based on Passively Collected Mobile Device Location Dataen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Yang_umd_0117N_20832.pdf
Size:
2.38 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Civil & Environmental Engineering Theses and Dissertations