Application of Machine Learning to the Analysis and Prediction of the Coincidence of Ground Delay Programs and Ground Stop

Abstract

Traffic Management Initiatives such as Ground Delay Programs and Ground Stops are implemented by traffic management personnel to control air traffic volume to constrained airports when traffic demand is projected to exceed the airports’ acceptance rate due to conditions such as inclement weather, volume constraints, etc. Ground Delay Programs are issued for lengthy periods of time and aircraft are assigned departure times later than scheduled. Ground Stops on the other hand, are issued for short periods of time and aircraft are not permitted to land at the constrained airport. Occasionally, Ground Stops are issued during an ongoing Ground Delay Program, and vice versa, which hinders the efficient planning and implementation of these Traffic Management Initiatives. This research proposes a methodology to help stakeholders better capture the impact of the coincidence of weather related Ground Delay Programs and Ground Stops, and potentially help reduce the number and duration of such coincidences. This is achieved by leveraging Machine Learning techniques to predict their coincidence at a given hour, predict which Traffic Management Initiative would precede the other during their coincidence, and identify key predictors that cause their coincidence. The Random Forests Machine Learning algorithm was identified as the best suited algorithm for predicting the coincidence of weather-related Ground Delay Programs and Ground Stops, as well as the Traffic Management Initiative that would precede the other during their coincidence.