A Methodology for Predicting Ground Delay Program Incidence through Machine Learning

Effective ground delay programs (GDP) are needed to intervene when there are bad weather or airport capacity issues. This paper proposes a new methodology for predicting the incidence of effective ground delay programs by utilizing machine learning techniques, which can improve the safety and economic benefits of flights. We use the combination of local weather and flight operation data along with the ATM airport performance (ATMAP) algorithm to quantify the weather and to generate an ATMAP score. We then compared the accuracy of three machine learning models, Support Vector Machine, Random Forest, and XGBoost, to estimate the probability of GDPs. The results of the weather analysis, performed by the ATMAP algorithm, indicated that the ceiling was the most critical weather factor. Lastly, we used two linear regression models (ridge and LASSO) and a non-linear regression model (decision tree) to predict departure flight delays during GDP. The predictive accuracy of the regression models was enhanced by an increase in ATMAP scores, with the decision tree model outperforming the other models, resulting in an improvement of 8.8% in its correlation coefficient ($R^2$).