With the recent upsurge in mental health concerns and ongoing isolation regulations brought about by the COVID-19 pandemic, it is important to understand how an individual's daily travel behavior can affect their mental health. Before finding any correlations to mental health, researchers must first have individual travel behavior information: an accurate number of activities and locations of those activities. One way to obtain daily travel behavior information is through the interpretation of cellular Global Positioning System (GPS) data. Previous methods that interpret GPS data into travel behavior information have limitations. Specifically, rule-based algorithms are structured around subjective rule-based tests, clustering algorithms include only spatial parameters that are chosen sequentially or require further exploration, and imputation algorithms are sensitive to provided context (input parameters) and/or require lots of training data to validate the results of the algorithm. Due to the lack of provided training data that would be required for an imputation algorithm, this thesis uses a previously adopted clustering method. The objective of this thesis is to determine which spatial, entropy, and time parameters cause the clustering algorithm to give the most accurate travel behavior results. This optimal set of parameters was determined using a comparison of two non-linear optimization methods: simulated annealing and a limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) optimizer. Ultimately, simulated annealing optimization found the best set of clustering parameters leading to 91% clustering algorithm accuracy whereas L-BFGS-B optimization found parameters that were only able to produce a maximum of 79% accuracy. Using the most optimal set of parameters in the clustering algorithm, an entire set of GPS data can be interpreted to determine an individual's daily travel behavior. This resulting individual travel behavior sets the groundwork to answer the question of how individual travel behavior can affect mental health.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-10764 |
| Date | 05 December 2022 |
| Creators | Riches, Gillian Michele |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | https://lib.byu.edu/about/copyright/ |
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