Open Source Repositories

The SynthAIr project provides a comprehensive suite of open-source tools for synthetic data generation and embedding-based analytics in Air Traffic Management. Our repositories are organized by functionality and data type, offering researchers and practitioners flexible solutions for various ATM applications.

🔗 Main GitHub Organization: https://github.com/SynthAIr

Tabular Data Generators

SynTabAIr

https://github.com/SynthAIr/syntabair

Generates synthetic tabular flight data from European datasets, implementing:

  • CTGAN (Conditional Tabular GAN)
  • TabSyn (Diffusion-based synthesis)
  • REaLTabFormer (Transformer-based generation)
  • Gaussian Copula methods
  • Comprehensive evaluation metrics for fidelity, privacy, and utility

License: CC BY-SA 4.0

SynFlyInf

https://github.com/SynthAIr/SynFlyInf

Focuses on generating synthetic flight information using U.S. Bureau of Transportation Statistics data:

  • TVAE (Tabular Variational Autoencoder) implementation
  • Gaussian Copula approaches
  • Jupyter notebook-based workflow for different use cases
  • Support for flight delay, turnaround time, and diversion prediction

License: CC BY-SA 4.0

Passenger Flow

https://github.com/SynthAIr/passenger_flow

Specialized for airport security checkpoint data generation:

  • TVAE model adapted for passenger flow patterns
  • Notebook-driven analysis and generation pipeline
  • Pre-trained models available for immediate use
  • Downstream task evaluation capabilities

License: MIT

Time Series Generators

DM_FM_Trajectories

https://github.com/SynthAIr/DM_FM_Trajectories

Implements state-of-the-art generative models for aircraft trajectory synthesis, featuring:

  • Multiple architectures: Diffusion models, Flow matching, and VAEs
  • Transfer learning capabilities for data-scarce scenarios
  • Weather integration for conditional generation
  • Comprehensive evaluation framework

License: CC BY-SA 4.0

TimeVQVAE_Trajectories

https://github.com/SynthAIr/TimeVQVAE_Trajectories

Advanced trajectory generation using vector quantization:

  • Time-frequency domain processing
  • Hierarchical generation for global and local patterns
  • Three-stage training process
  • Transformer-based priors for temporal modeling

License: CC BY-SA 4.0

TimeGAN_Trajectories

https://github.com/SynthAIr/TimeGAN_Trajectories

Specialized for landing trajectory generation at terminal maneuvering areas:

  • Time-series GAN architecture capturing both static and temporal features
  • Optimized for approach patterns and runway-specific sequences
  • K-means clustering with Dynamic Time Warping for trajectory categorization
  • Three-module structure: preprocessing, model training, and evaluation

License: CC0-1.0

Embedding Models

AeroEmbed

https://github.com/SynthAIr/AeroEmbed

A framework for extracting and analyzing embeddings from flight operational data using TabSyn. This repository enables:

  • Extraction of meaningful representations from mixed-type flight records
  • Operational pattern discovery and anomaly detection
  • Clustering analysis and carrier profiling
  • Comprehensive visualization tools for embedding space exploration

License: CC BY-SA 4.0

Getting Started

Each repository includes:

  • Detailed installation instructions using Poetry or pip
  • Comprehensive documentation and usage examples
  • Pre-processed datasets or data preparation scripts
  • Evaluation frameworks and visualization tools
  • Example notebooks demonstrating key functionalities

Citation

When using these tools in your research, please cite the relevant SynthAIr publications/deliverables and acknowledge the specific repository used. Detailed citation information is available in each repository’s README file.

Support

For questions, issues, or collaboration opportunities:

  • Open an issue in the relevant GitHub repository
  • Consult the documentation within each repository
  • Visit our project website at https://synthair.github.io/

These repositories represent ongoing research in synthetic data generation for ATM. We encourage the community to explore, use, and extend these tools to advance the field of AI-driven air traffic management.


© 2025 - SynthAIr