(Georgia Institute of Technology, 2019-06)
Mangortey, Eugene; Pinon, Olivia J.; Puranik, Tejas G.; Mavris, Dimitri N.
Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) are instituted by traffic management personnel to address and reduce the impacts of constraints in the National Airspace System. Ground Delay Programs are initiated whenever demand is projected to exceed an airport’s acceptance rate over a lengthy period of time. Such instances occur when an airport is affected by conditions such as inclement weather, aircraft congestion, runway-related incidents, equipment failures, and other causes that do not fall in these categories. Over the years, efforts have been made to reduce the impact of Ground Delay Programs on airports and flight operations by predicting their occurrence. However, these efforts have largely focused on weather-related Ground Delay Programs, primarily due to a lack of access to comprehensive Ground Delay Program data. There has also been limited benchmarking of Machine Learning algorithms to predict the occurrence of Ground Delay Programs. Consequently, this research 1)fused data from the Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraged supervised Machine Learning algorithms to develop prediction models as a means to predict the occurrence of weather and volume-related Ground Delay Programs. The Kappa Statistic evaluation metric revealed that Boosting Ensemble was the best suited algorithm for predicting the occurrence of weather and volume-related Ground Delay Programs.