Pre-Tactical Flight-Delay and Turnaround Forecasting with Synthetic Aviation Data

TL;DR

This study demonstrates that high-quality synthetic aviation data can effectively train predictive models for pre-tactical flight delays and turnaround times, preserving most real-data performance and operational insights.

Contribution

It introduces a methodology for using neural network-based synthetic data generators to replace real data in aviation predictive modeling, enabling privacy-preserving analytics.

Findings

Transformer-based synthetic data generators retain 94-97% of real-data predictive performance.

Synthetic data preserves key operational relationships and feature importance patterns.

Prediction accuracy is limited by the stochastic nature of flight operations, even with real data.

Abstract

Access to comprehensive flight operations data remains severely restricted in aviation due to commercial sensitivity and competitive considerations, hindering the development of predictive models for operational planning. This paper investigates whether synthetic data can effectively replace real operational data for training machine learning models in pre-tactical aviation scenarios-predictions made hours to days before operations using only scheduled flight information. We evaluate four state-of-the-art synthetic data generators on three prediction tasks: aircraft turnaround time, departure delays, and arrival delays. Using a Train on Synthetic, Test on Real (TSTR) methodology on over 1.7 million European flight records, we first validate synthetic data quality through fidelity assessments, then assess both predictive performance and the preservation of operational relationships. Our results show that advanced neural network architectures, specifically transformer-based generators, can retain 94-97% of real-data predictive performance while maintaining feature importance patterns informative for operational decision-making. Our analysis reveals that even with real data, prediction accuracy is inherently limited when only scheduled information is available-establishing realistic baselines for pre-tactical forecasting. These findings suggest that high-quality synthetic data can enable broader access to aviation analytics capabilities while preserving commercial confidentiality, though stakeholders must maintain realistic expectations about pre-tactical prediction accuracy given the stochastic nature of flight operations.