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.


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    Title :

    Synthetic Flight Data Generation Using Generative Models


    Contributors:


    Publication date :

    2025-04-08


    Size :

    914348 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SYNTHETIC FLIGHT PERFORMANCE DATA GENERATION

    BERNA ANTONIO GRACIA / MASAVEU CARLOS QUEREJETA / LEONES JAVIER LOPEZ | European Patent Office | 2024

    Free access