The objective of the thesis is to understand the factors affecting spatiotemporal ridehailing demand patterns as the COVID-19 pandemic has evolved. Specifically, the current study examines the key contributing factors of weekly ridehailing demand by employing Taxi and Transportation Network Companies (TNC) trip data from January 2019 through December 2020 for New York City. The ridehailing demand is partitioned across four time periods including Morning Peak, Morning Off Peak, Evening Peak and Evening Off Peak to accommodate for the time-of-day specific variations. Drawing on the high-resolution NYC data, the current study developed pooled spatial panel models to accommodate for the spatial and temporal heterogeneity. The thesis employs a recasting approach that enables the estimation of a parsimonious model specification across the four time periods. Two recasted spatial models: 1) Spatial Lag Model and 2) Spatial Error Model are estimated for ridehailing demand across the two services - Taxi and TNC - while considering a comprehensive list of factors including COVID-19 pandemic attributes, sociodemographic characteristics, land use and built environment attributes, transportation infrastructure and weather attributes. The model estimation results are further augmented with a robust policy analysis to predict potential ridehailing demand for future months. The policy exercise also illustrates how the proposed model can be employed by ridehailing companies and transportation agencies to examine ridehailing demand evolution as the pandemic continues.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2631 |
| Date | 01 January 2022 |
| Creators | Parvez, Dewan Ashraful |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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