Term of Award
Spring 2024
Degree Name
Master of Science, Computer Science (M.S.C.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Computer Science
Committee Chair
Yao Xu
Committee Member 1
Lixin Li
Committee Member 2
Kai Wang
Abstract
Accurate prediction of flight delays remains a formidable challenge within the aviation industry, owing to its inherent complexity and the interconnectivity of its operations. Traditional flight prediction methods frequently utilize meteorological conditions—such as temperature, humidity, and dew point—alongside flight-specific data like departure and arrival times. However, these predictors often fall short of capturing the nuanced dynamics that lead to delays. This thesis introduces network centrality measures as novel predictors for enhancing the binary classification of flight arrival delays. Furthermore, it emphasizes the application of tree-based ensemble models, which are recognized for their superior ability to model complex relationships compared to single-base classifiers. Empirical testing reveals that incorporating centrality measures notably improves the models' average performance. The most effective model achieves an accuracy rate of 86%, surpassing the baseline accuracy by 2%.
OCLC Number
1430435244
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916564849102950
Recommended Citation
Ajayi, Joseph, "Enhancing Flight Delay Predictions Using Network Centrality Measures" (2024). Electronic Theses and Dissertations. 2705.
https://digitalcommons.georgiasouthern.edu/etd/2705
Research Data and Supplementary Material
No
