In this dissertation, we examine two dimensions of domestic aviation - demand and delay - that directly influence economic impact of the sector. We conduct a comprehensive analysis of airline demand employing airline data compiled by Bureau of Transportation Statistics. The demand analysis is conducted in three steps. First, we propose a novel modeling approach for modeling airline demand evolution over time. Specifically, we develop a joint panel group generalized ordered probit (GGOP) model system for modeling air passenger arrivals and departures in a discretized framework that subsumes the traditional linear regression approach. Further, we consider the influence of observed and unobserved effects on airline demand across multiple time periods. Second, we explore the impact of Coronavirus disease 2019 (COVID-19) on domestic airline demand in the US. The effect of COVID-19 on airline demand is identified by considering global and local COVID-19 transmission, temporal indicators of pandemic start and progress, and interactions of airline demand predictors with global and local COVID-19 indicators. Based on the results, we present a blueprint for airline demand recovery using three hypothetical scenarios of COVID-19 transmission rates – expected, pessimistic and optimistic. Finally, we build on the novel airline demand modeling framework by accommodating for observed and unobserved spatial and temporal effects. Specifically, we develop spatial lag model and spatial error model formulations of the GGOP model proposed in the first step. The second part of the dissertation is focused on flight level delay analysis. In this part, we identify the factors affecting flight level airline delay by jointly modeling departure and arrival delays. Towards this end, we develop a novel copula-based group generalized ordered logit model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2490 |
| Date | 01 January 2022 |
| Creators | Tirtha, Sudipta Dey |
| 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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