Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures
This work addresses the need for accurate traffic models in air traffic management design and validation, but it is incremental as it builds on existing probabilistic modeling techniques.
The authors tackled the problem of generating realistic aircraft trajectory models for air traffic management by learning variability from flight tracks and procedures, using a Gaussian mixture model to capture deviations and pairwise correlations, and demonstrated the method on arrival tracks at JFK airport with distributional similarity evaluated via Jensen-Shannon divergence.
Realistic aircraft trajectory models are useful in the design and validation of air traffic management (ATM) systems. Models of aircraft operated under instrument flight rules (IFR) require capturing the variability inherent in how aircraft follow standard flight procedures. The variability in aircraft behavior differs among flight stages. In this paper, we propose a simple probabilistic model that can learn this variability from procedural data and flight tracks collected from radar surveillance data. For each segment, we use a Gaussian mixture model to learn the deviations of aircraft trajectories from their procedures. Given new procedures, we generate synthetic trajectories by sampling a series of deviations from the Gaussian mixture model and reconstructing the aircraft trajectory using the deviations and the procedures. We extend this method to capture pairwise correlations between aircraft and show how a pairwise model can be used to generate traffic involving an arbitrary number of aircraft. We demonstrate the proposed models on the arrival tracks and procedures of the John F. Kennedy International Airport. Distributional similarity between the original and the synthetic trajectory dataset was evaluated using the Jensen-Shannon divergence between the empirical distributions of different variables and we provide qualitative analyses of the synthetic trajectories generated.