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

2025-08-04

AI Summary

Aviation’s commercial sensitivity impedes access to real-world flight operations data, hindering the development of pre-tactical (hours to days before flight execution) delay and turn-around time prediction models based solely on scheduled information. To address this, we propose a Transformer-based synthetic data generation framework that produces high-fidelity synthetic datasets using only planned flight attributes. Adopting a Train-on-Synthetic-Test-on-Real (TSTR) paradigm, we systematically evaluate departure delay, arrival delay, and turn-around time prediction across over 1.7 million European flights using four state-of-the-art generative models. Results show that the best synthetic data preserves 94%–97% of the predictive performance attainable with real data, accurately reproduces feature importance rankings, and ensures privacy preservation and model interpretability. Moreover, our analysis reveals an inherent accuracy ceiling for purely schedule-driven pre-tactical prediction—establishing the first empirical benchmark for this domain.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Overcoming restricted flight data access for predictive modeling
• Evaluating synthetic data for flight delay and turnaround predictions
• Assessing synthetic data fidelity and predictive performance

Innovation

• Uses synthetic data for aviation forecasting
• Evaluates four synthetic data generators
• Transformer-based generators retain 94-97% performance