UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2020 - 20222

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Subject: search.filters.subject.Mobile Device Location Data

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    A NOVEL MEASUREMENT OF JOB ACCESSIBILITY BASED ON MOBILE DEVICE LOCATION DATA
    (2022) Zhao, Guangchen; Zhang, Lei LZ; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mobile device location data (MDLD) can offer a new perspective on measuring accessibility. Compared with the traditional accessibility measures, MDLD is capable of capturing people’s preferences with the observed locations. This study proposes a job accessibility measure based on the identified home and work locations from MDLD, evaluating the job accessibility by the proportion of workers identified working in zones within a certain time threshold. In the case study on the Baltimore region, the job accessibility from the MDLD-based measure is compared with the results from a widely-used traditional measure. Then, generalized additive models (GAM) are built to analyze the socio-demographic impact on job accessibility from a MDLD-based measure and a traditional measure, with a feature-to-feature comparison. Finally, the socio-demographic characteristics of regions where there are major disparities between the job accessibility from the traditional measure and the MDLD-based measure are also evaluated from the Student's t-test results.
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    Multimodal Travel Mode Imputation based on Passively Collected Mobile Device Location Data
    (2020) Yang, Mofeng; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Passively 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.