dc.contributor.advisor | Gilligan, Jonathan | |
dc.contributor.advisor | Baroud, Hiba | |
dc.creator | Martínez , Juan Camilo Camilo | |
dc.date.accessioned | 2023-05-17T20:52:51Z | |
dc.date.available | 2023-05-17T20:52:51Z | |
dc.date.created | 2023-05 | |
dc.date.issued | 2023-04-03 | |
dc.date.submitted | May 2023 | |
dc.identifier.uri | http://hdl.handle.net/1803/18259 | |
dc.description.abstract | In the United States alone, people take more than 9 billion trips on public transportation every year. Such modes of transport play an important role in access to healthcare, education, and economic opportunities. As a result, cities strive to optimize transit to meet the needs of their citizens. A fundamental requirement for optimizing transit lines in predicting demand in terms of expected board counts and occupancy, which in turn can inform decision-making. Predicting occupancy is also particularly important as we deal with a global pandemic since crowding in transit must be avoided to maintain social distancing protocols. We develop data-driven modeling strategies to predict board counts at individual bus stops as well as maximum occupancy in a trip. We show how off-the-shelf statistical as well as algorithmic approaches fail to work in this scenario due to high sparsity in data (excess zero counts) and multi-modal characteristics. We propose a hierarchical zero-inflated random forest model that can handle excess zero counts and learn from a rich variety of features to forecast board counts. Finally, we use real-world transportation data from the city of Chattanooga, TN to validate our approach. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Public Transportation, Imbalanced Data, Machine Learning, COVID-19 Pandemic. | |
dc.title | Master of Science | |
dc.type | Thesis | |
dc.date.updated | 2023-05-17T20:52:51Z | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | Masters | |
thesis.degree.discipline | Environmental Engineering | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
dc.creator.orcid | 0000-0002-2881-3076 | |