# Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data

arXiv: https://arxiv.org/abs/1810.09568 · HTML: https://mlsanity.com/paper/1810.09568/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Inferring Traffic Models in Terminal Airspace from Flight Tracks and Procedures](https://mlsanity.com/paper/2303.09981/) (2023)
- [Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace](https://mlsanity.com/paper/2409.17359/) (2024)
- [Multi-Agent Inverted Transformer for Flight Trajectory Prediction](https://mlsanity.com/paper/2509.21004/) (2025)
- [Phased Flight Trajectory Prediction with Deep Learning](https://mlsanity.com/paper/2203.09033/) (2022)
- [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)
- [Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications](https://mlsanity.com/paper/2401.14421/) (2024)
- [DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction](https://mlsanity.com/paper/2503.04823/) (2025)
- [DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers](https://mlsanity.com/paper/2610.16959/) (2026)

## Abstract (arXiv)

Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport. The method fits the model based on a historical dataset of radar-based position measurements of aircraft landings and takeoffs at that airport. We find that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of aircraft trajectories. Furthermore, the model trains quickly, is compact, and allows for efficient real-time inference.