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Realistic aircraft trajectory models are useful in the design and validationof air traffic management (ATM) systems. Models of aircraft operated underinstrument flight rules (IFR) require capturing the variability inherent in howaircraft follow standard flight procedures. The variability in aircraftbehavior varies among flight stages. In this paper, we propose a probabilisticmodel that can learn the variability from the procedural data and flight trackscollected from radar surveillance data. For each segment, a Gaussian mixturemodel is used to learn the deviations of aircraft trajectories from theirprocedures. Given new procedures, we can generate synthetic trajectories bysampling a series of deviations from the trained Gaussian distributions andreconstructing the aircraft trajectory using the deviations and the procedures.We extend this method to capture pairwise correlations between aircraft andshow how a pairwise model can be used to generate traffic involving anarbitrary number of aircraft. We demonstrate the proposed models on the arrivaltracks and procedures of the John F. Kennedy International Airport. Thedistributional similarity between the original and the synthetic trajectorydataset was evaluated using the Jensen-Shannon divergence between the empiricaldistributions of different variables. We also provide qualitative analyses ofthe synthetic trajectories generated from the models.

Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures