CYCLING AROUND THE CLOCK: MODELING BIKE SHARE TRIPS AS HIGH-FREQUENCY SPATIAL INTERACTIONS
dc.contributor.advisor | Oshan, Taylor | en_US |
dc.contributor.author | Liu, Zheng | en_US |
dc.contributor.department | Geography/Library & Information Systems | en_US |
dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
dc.date.accessioned | 2023-10-06T05:38:48Z | |
dc.date.available | 2023-10-06T05:38:48Z | |
dc.date.issued | 2023 | en_US |
dc.description.abstract | Spatial interactions provide insights into urban mobility that reflects urban livability. A range of traditional and modern urban mobility models have been developed to analyze and model spatial interaction. The study of bike-sharing systems has emerged as a new area of research, offering expanded opportunities to understand the dynamics of spatial interaction processes. This dissertation proposes new methods and frameworks to model and understand the high-frequency changes in the spatial interaction of a bike share system. Three challenges related to the spatial and temporal dynamics of spatial interaction within a bike share system are discussed via three studies: 1) Predicting spatial interaction demand at new stations as part of system infrastructure expansion; 2) Understanding the dynamics of determinants in the context of the COVID-19 pandemic; and 3) Detecting events that lead to changes in the spatial interaction process of bike share trips from a model-based proxy. The first study proposes a hybrid strategy to predict 'cold start' trips by comparing flow interpolation and spatial interaction methods. The study reveals 'cold start' stations with different classifications based on their locations have different best model choices as a hybrid strategy for the research question. The second study demonstrates a disaggregated comparative framework to capture the dynamics of determinants in bike share trip generation before, during, and after the COVID-19 lockdown and to identify long-term bike share usage behavioral changes. The third study investigates an event detection approach combining martingale test and spatial interaction model with specification evaluation from simulated data and explorative examination from bike share datasets in New York City, Washington, DC, and San Francisco. Results from the study recognize events from exogenous factors that induced changes in spatial interactions which are critical for model evaluation and improvement toward more flexible models to high-frequency changes. The dissertation elaborated and expanded the spatial interaction model to more effectively meet the research demands for the novel transportation mode of bike-share cycling in the context of a high-frequency urban environment. Taken as a whole, this dissertation contributes to the field of transportation geography and geographic information science and contributes methods toward the creation of improved transport systems for more livable cities. | en_US |
dc.identifier | https://doi.org/10.13016/dspace/khgi-9h4n | |
dc.identifier.uri | http://hdl.handle.net/1903/30748 | |
dc.language.iso | en | en_US |
dc.subject.pqcontrolled | Geographic information science and geodesy | en_US |
dc.subject.pquncontrolled | Bike share | en_US |
dc.subject.pquncontrolled | COVID-19 | en_US |
dc.subject.pquncontrolled | Event detection | en_US |
dc.subject.pquncontrolled | Flow interpolation | en_US |
dc.subject.pquncontrolled | Micro-mobility | en_US |
dc.subject.pquncontrolled | Spatial interaction model | en_US |
dc.title | CYCLING AROUND THE CLOCK: MODELING BIKE SHARE TRIPS AS HIGH-FREQUENCY SPATIAL INTERACTIONS | en_US |
dc.type | Dissertation | en_US |
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