A microscopic model employs a number of machine learning classifiers on trajectories of aircraft on approach in order to predict if a GA will be performed. A macroscopic model quantifies the probability of a GA within the next hour for an airport by applying a generalized additive model. In this study, two different modeling methods for predicting the occurrence of GAs based on open-source Automatic Dependent Surveillance–Broadcast (ADS-B) and meteorological data are presented. The incidence of a GA can subsequently affect the workload of flight crews and air traffic controllers, and might impact an airport runway’s throughput capacity.
Go-arounds (GAs) are standard air traffic control procedures during which aircraft approach a runway but do not land.
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