# Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach

arXiv: https://arxiv.org/abs/1812.11670 · HTML: https://mlsanity.com/paper/1812.11670/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility](https://mlsanity.com/paper/2206.04513/) (2022)
- [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)
- [Phased Flight Trajectory Prediction with Deep Learning](https://mlsanity.com/paper/2203.09033/) (2022)
- [Categorizing Flight Paths using Data Visualization and Clustering Methodologies](https://mlsanity.com/paper/2310.00773/) (2023)
- [Landing Trajectory Prediction for UAS Based on Generative Adversarial Network](https://mlsanity.com/paper/2411.14403/) (2024)
- [Effective and Efficient Representation Learning for Flight Trajectories](https://mlsanity.com/paper/2412.16581/) (2024)
- [A Neural ODE Approach to Aircraft Flight Dynamics Modelling](https://mlsanity.com/paper/2509.23307/) (2025)

## Abstract (arXiv)

Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system. Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity meteorological datasets - wind, temperature and convective weather. We then model the track points on trajectories as conditional Gaussian mixtures with parameters to be learned from our proposed deep generative model, which is an end-to-end convolutional recurrent neural network that consists of a long short-term memory (LSTM) encoder network and a mixture density LSTM decoder network. The encoder network embeds last-filed flight plan information into fixed-size hidden state variables and feeds the decoder network, which further learns the spatiotemporal correlations from the historical flight tracks and outputs the parameters of Gaussian mixtures. Convolutional layers are integrated into the pipeline to learn representations from the high-dimension weather features. During the inference process, beam search, adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to prune the variance of generated trajectories.