Predicting the initiation of a ground delay program

Ground delay programs (GDPs) are often initiated in the U.S. National Airspace System to balance demand with capacity at a capacity-constrained arrival airport. A GDP assigns departure delay at origin airports to modulate demand at the destination airport. Usually, one GDP affects hundreds of flights. The substantial impact of GDPs on flight operations leads to our research interest in predicting GDP initiation. In this paper, we identify variables that play a significant role on GDP initiation decisions and quantify their impact using logistic regression. We consider lead times of from 1 to 4 hours and specify a logistic model for each lead time. This allows us to provide a GDP initiation prediction for flight operators for up to a 4-hour time horizon. Further, using cross-validation, we compare the predictions of these models, including a weighted accuracy, true positive rate, and precision. We find that the GDP initiation predictions over the longer time horizon are only slightly less reliable than that of one hour into the future. Whatever the time horizon, however, the model predictions are often incorrect, either predicting a GDP when one is not implemented or vice versa.