This dissertation conducted an extensive examination of dockless e-scooter dynamics using high-resolution trip data from Austin, Texas. Four studies were conducted to capture the multifaceted nature of e-scooter operations and demand. The first study aimed to identify and quantify the influence of contributing factors affecting e-scooter demand by partitioning the data by time period for weekdays and weekends. Utilizing a joint panel linear regression (JPLR) model, significant associations were observed between e-scooter demand and variables such as sociodemographic attributes, transportation infrastructure, land use, meteorological attributes, and situational factors. The second study shifted focus to shared e-scooter origin-destination (OD) flows in the urban region. By employing a joint binary logit-fractional split model, e-scooter OD flows were analyzed, emphasizing variations across distinct time periods and the subsequent implications for e-scooter deployment and rebalancing strategies. The third study delved into e-scooter utilization efficiency, introducing a time-to-book (TtB) measure. Through a Mixed Grouped Ordered Logit (MGOL) model, the study highlighted variations between regular and peak weeks, offering operators a chance to enhance fleet utilization. The final study addressed the broader context of the e-scooter industry, investigating the impact of the COVID-19 pandemic. By analyzing datasets spanning January 2019 through December 2021, a spatial approach illuminated changes in e-scooter demand patterns before, during, and after the pandemic, highlighting the effects of COVID-19-related factors and vaccine attributes on e-scooter trends. These collective insights from the four studies provide valuable contributions to understanding and enhancing e-scooter operations in urban landscapes
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1023 |
| Date | 01 January 2023 |
| Creators | Alsulami, Nami |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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