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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Computer Science

Committee Chair

Yao Xu

Committee Member 1

Lixin Li

Committee Member 2

Kai Wang


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%.

Research Data and Supplementary Material


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Data Science Commons