[Poster] Classification of Holdings in Flights Arriving at Dubai International Airport (DXB) in One Year

Abstract

The purpose of this study was to use a machine-learning approach for the classification of holdings for all flights arriving at Dubai International Airport (IATA DXB) in a period of one year. The study used data from ADS-B for all flights arriving and departing DXB for the period 15 February 2018 and 15 February 2019. For all 189,999 arrivals analyzed, it was identified the Standard Terminal Arrival Route (STAR) flown, the occurrence of holdings, the aircraft flying (for the determination of wake turbulence category), the visibility, and data related to the interception of a circle centered around DXB with a radius of 50 NM (geographical position, number of aircraft entering the circle for several time windows, number of aircraft flying within this circle etc), along with the number of aircraft taking-off and landing during a given time period. These features were tested, in different combinations, to determine their impact on metrics used to evaluate the classifier output quality. After running classifiers designed with different algorithms and combinations of features, it was identified the one which, with the CatBoost algorithm, gave the best F1 (0.777), Precision (0.847) and Accuracy (0.912) for a period of 10 minutes after interception. The F1 increased to 0.85 when the altitude and the speed at the interception of the circle were included as features, but they were discarded as they introduced an undesirable bias in the final model.