Synthetic Flight Data Generation Using Generative Models

• URL: http://arxiv.org/abs/2604.20293v1
• Date: Wed, 22 Apr 2026 07:46:23 GMT
• Title: Synthetic Flight Data Generation Using Generative Models
• Abstract summary: This study investigates the potential of generative models to produce realistic synthetic flight data.<n>The need for synthetic flight data arises from their potential to serve as an alternative to confidential real-world records.<n>Two generative models, Tabular Variational Autoencoder (TVAE) and Gaussian Copula (GC) are adapted to generate synthetic flight information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework [0.0]
  Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency.<n>This study investigates how generative models can augment historical flight data with synthetic diversion records to enhance model training and improve predictive accuracy.
  arXiv  Detail & Related papers  (2026-04-22T07:35:11Z)
• Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era [49.46005489386284]
  This tutorial introduces the foundations and latest advances in synthetic data generation.<n> Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice.
  arXiv  Detail & Related papers  (2025-08-27T05:04:07Z)
• Valid Inference with Imperfect Synthetic Data [39.10587411316875]
  We introduce a new estimator based on generalized method of moments.<n>We find that interactions between the moment residuals of synthetic data and those of real data can greatly improve estimates of the target parameter.
  arXiv  Detail & Related papers  (2025-08-08T18:32:52Z)
• AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection [3.5912245880418125]
  Training fall detection systems is challenging due to the scarcity of real-world fall data, particularly from elderly individuals.<n>This study evaluates text-to-motion and text-to-text models in simulating realistic fall scenarios.<n>We generate synthetic datasets and integrate them with four real-world baseline datasets to assess their impact on fall detection performance.
  arXiv  Detail & Related papers  (2025-05-07T02:30:33Z)
• Scaling Laws of Synthetic Data for Language Models [125.41600201811417]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• Evaluating the Impact of Synthetic Data on Object Detection Tasks in Autonomous Driving [0.0]
  We compare 2D and 3D object detection tasks trained on real, synthetic, and mixed datasets.<n>Our findings demonstrate that the use of a combination of real and synthetic data improves the robustness and generalization of object detection models.
  arXiv  Detail & Related papers  (2025-03-12T20:13:33Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments [42.8983261737774]
  We investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models.
  arXiv  Detail & Related papers  (2024-06-07T12:36:31Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• Boosting Data Analytics With Synthetic Volume Expansion [3.568650932986342]
  This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data. A key finding within this framework is the generational effect, which reveals that the error rate of statistical methods on synthetic data decreases with the addition of more synthetic data but may eventually rise or stabilize.
  arXiv  Detail & Related papers  (2023-10-27T01:57:27Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction [3.3139597764446607]
  In machine learning, generative models are a class of methods capable of learning data distribution to generate artificial data. In this study, we developed a GAN model to generate synthetic time series to balance our limited recorded time series data. The aim is to predict the flow using precipitation data and examine the impact of data augmentation using synthetic data in model performance.
  arXiv  Detail & Related papers  (2023-01-31T16:12:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.