Predicting the occurrence of ground delay programs and their impact on airport and flight operations

Abstract

A flight is delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common delays and can be caused by the initiation of Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP). A Ground Delay Program is implemented to control air traffic volume to an airport over a lengthy period when traffic demand is projected to exceed the airport's acceptance rate due to conditions such as inclement weather, volume constraints, closed runways or equipment failures. Ground Delay Programs cause flight delays which affect airlines, passengers, and airport operations. Consequently, various efforts have been made to reduce the impacts of Ground Delay Programs by predicting their occurrence or the optimal time for initiating Ground Delay Programs. However, a few research gaps exist. First, most of the previous efforts have focused on only weather-related Ground Delay Programs, ignoring other causes such as volume constraints and runway-related incidents. Second, there has been limited benchmarking of Machine Learning techniques to predict the occurrence of Ground Delay Programs. Finally, little to no work has been conducted to predict the impact of Ground Delay Programs on flight and airport operations such as their duration, flight delay times, and taxi-in time delays. This research addresses these gaps by 1) fusing data from a variety of datasets (Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS)) and 2) leveraging and benchmarking Machine Learning techniques to develop prediction models aimed at reducing the impacts of Ground Delay Programs on flight and airport operations. These models predict 1) flight delay times due to a Ground Delay Program, 2) the duration of a Ground Delay Program, 3) the impact of a Ground Delay Program on taxi-in time delays, and 4) the occurrence of Ground Delay Programs. Evaluation metrics such as Mean Absolute Error, Root mean Squared Error, Correlation, and R-square revealed that Random Forests was the optimal Machine Learning technique for predicting flight delay times due to Ground Delay Programs, the duration of Ground Delay Programs, and taxi-in time delays during a Ground Delay Program. On the other hand, the Kappa Statistic revealed that Boosting Ensemble was the optimal Machine learning technique for predicting the occurrence of Ground Delay Programs. The aforementioned prediction models may help airlines, passengers, and air traffic controllers to make more informed decisions which may lead to a reduction in Ground Delay Program related-delays and their impacts on airport and flight operations.