# Synthetic Flight Data Generation Using Generative Models

arXiv: https://arxiv.org/abs/2604.20293 · HTML: https://mlsanity.com/paper/2604.20293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [Lagging Inference Networks and Posterior Collapse in Variational Autoencoders](https://mlsanity.com/paper/1901.05534/) (2019)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Robust Variational Autoencoder for Tabular Data with Beta Divergence](https://mlsanity.com/paper/2006.08204/) (2020)
- [Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula](https://mlsanity.com/paper/1902.01499/) (2019)
- [Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance](https://mlsanity.com/paper/2010.13456/) (2020)
- [InVAErt networks: a data-driven framework for model synthesis and identifiability analysis](https://mlsanity.com/paper/2307.12586/) (2023)
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- [A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework](https://mlsanity.com/paper/2406.01950/) (2024)
- [Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2310.13925/) (2023)
- [Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations](https://mlsanity.com/paper/2411.10982/) (2024)
- [The Intriguing Properties of Model Explanations](https://mlsanity.com/paper/1801.09808/) (2018)
- [Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck](https://mlsanity.com/paper/2004.10603/) (2020)
- [Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization](https://mlsanity.com/paper/2110.15145/) (2021)
- [An Exploratory Assessment of LLM's Potential Toward Flight Trajectory Reconstruction Analysis](https://mlsanity.com/paper/2401.06204/) (2024)
- [Discouraging posterior collapse in hierarchical Variational Autoencoders using context](https://mlsanity.com/paper/2302.09976/) (2023)

## Abstract (arXiv)

The increasing adoption of synthetic data in aviation research offers a promising solution to data scarcity and confidentiality challenges. This study investigates the potential of generative models to produce realistic synthetic flight data and evaluates their quality through a comprehensive four-stage assessment framework. The need for synthetic flight data arises from their potential to serve as an alternative to confidential real-world records and to augment rare events in historical datasets. These enhanced datasets can then be used to train machine learning models that predict critical events, such as flight delays, cancellations, diversions, and turnaround times. Two generative models, Tabular Variational Autoencoder (TVAE) and Gaussian Copula (GC), are adapted to generate synthetic flight information and compared based on their ability to preserve statistical similarity, fidelity, diversity, and predictive utility. Results indicate that while GC achieves higher statistical similarity and fidelity, its computational cost hinders its applicability to large datasets. In contrast, TVAE efficiently handles large datasets and enables scalable synthetic data generation. The findings demonstrate that synthetic data can support flight delay prediction models with accuracy comparable to those trained on real data. These results pave the way for leveraging synthetic flight data to enhance predictive modeling in air transportation.