TY - JOUR
T1 - Enhancing Flight Delay Predictions Using Network Centrality Measures
AU - Ajayi, Joseph
AU - Xu, Yao
AU - Li, Lixin
AU - Wang, Kai
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/9
Y1 - 2024/9
N2 - Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.
AB - Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.
KW - CatBoost
KW - flight delay prediction
KW - gradient boosting
KW - machine learning
KW - network centrality
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85205258331&partnerID=8YFLogxK
U2 - 10.3390/info15090559
DO - 10.3390/info15090559
M3 - Article
AN - SCOPUS:85205258331
SN - 2078-2489
VL - 15
JO - Information (Switzerland)
JF - Information (Switzerland)
IS - 9
M1 - 559
ER -