Predicting Ground Delay Program At An Airport Based On Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of a ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Predicting the occurrence of ground delay programs can help the Federal Aviation Administration traffic managers and airline dispatchers prepare mitigation strategies to reduce impact of adverse weather. The models are developed for two major U.S. airports: Newark Liberty and San Francisco International airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. The decision tree model, on the other hand, classifies whether or not a ground delay program is likely during an hour based on the input variables. Results indicate both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at Newark Liberty Intl. airport, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay program occurrence at San Francisco Intl. airport.