# Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures

arXiv: https://arxiv.org/abs/2303.09981 · HTML: https://mlsanity.com/paper/2303.09981/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data](https://mlsanity.com/paper/1810.09568/) (2018)
- [Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach](https://mlsanity.com/paper/1812.11670/) (2018)
- [Improving Route Choice Models by Incorporating Contextual Factors via Knowledge Distillation](https://mlsanity.com/paper/1903.11253/) (2019)
- [Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning](https://mlsanity.com/paper/2005.07960/) (2020)

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Inverted Transformer for Flight Trajectory Prediction](https://mlsanity.com/paper/2509.21004/) (2025)
- [Aircraft Trajectory Dataset Augmentation in Latent Space](https://mlsanity.com/paper/2506.07585/) (2025)
- [Probabilistic Multi-Agent Aircraft Landing Time Prediction](https://mlsanity.com/paper/2512.08281/) (2025)
- [DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers](https://mlsanity.com/paper/2610.16959/) (2026)

## Abstract (arXiv)

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.