SyntAIr - IMPROVED ATM AUTOMATION AND SIMULATION THROUGH AI-BASED UNIVERSAL MODELS FOR SYNTHETIC DATA GENERATION

Informazioni relative al progetto

SynthAIR

ID dell’accordo di sovvenzione: 101114847

DOI

10.3030/101114847

Progetto chiuso

Data della firma CE 15 Giugno 2023

Data di avvio 1 Settembre 2023

Data di completamento 28 Febbraio 2026

Finanziato da

• Climate, Energy and Mobility

Costo totale

€ 1 215 003,75

Contributo UE

€ 993 550,00

Coordinato da

SINTEF AS
Norway

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Other (4)

"Initial generall time series embeddings ML model #1"

The initial general time series embeddings model will be open sourced.

Final Synthetic data generation ML models

A set of final AI methods for synthetic data generation will be open sourced.

"Initial Synthetic data generation ML models #1"

A set of initial AI methods for synthetic data generation will be open sourced.

General time series embeddings ML model

The final general time series embeddings model will be open sourced.

Documents, reports (11)

Final version of the CDE

This deliverable will provide an updated and final version of the CDE at M30 (including 6 months of D&C after the end of the project)

General time series embeddings report

This document will describe experiments performed with the developed general time series embeddings models, the results, and implications.

Exploratory research plan (ERP)

The exploratory research plan describes the way in which one or more validation exercises or activities are to be prepared and executed in order to achieve the validation objectives of an Exploratory Research project.

Synthetic data generation report

This document will describe the developed synthetic data generation model, experiments performed, results and implications with focus on use of synthetic data on downstream ML tasks.

Exploratory research report (ERR)

The exploratory research report consolidates the results obtained by an exploratory research project once the validation activities, experiments, etc, have been completed. This deliverable will include the outcome of the Task 5.2, 5.3 and 5.4

"Updated version of the CDEP (#2)"

This deliverable will provide an updated version of the CDE (plan and report) at M24

Definition of use cases

This deliverable will provide a description of the five use cases considered in SynthAIr and of the related datasets.

Communication, Dissemination, Exploitation Plan (CDEP)

This document will describe the CDE strategy and detail the related activities, following a coordinated plan (the deliverable will be released at M3 with the plan, and during the project with intermediate and final results)

Concept outline

The concept outline defines the general approach to how to solve the challenges addressed in the project. The concept outline includes the hypotheses that will be tested in the project and the assumptions about its expected results according to the specific TRL.

"Updated version of the CDEP (#1)"

This deliverable will provide an updated version of the CDEP (plan and report) at M12

SoTA

This deliverable will provide a review of the state of the art for the five use cases considered in the project and of related to them modelling techniques

Data sets, microdata, etc (1)

Synthetic ATM dataset

A set of synthetic ATM dataset will be generated and published.

Pubblicazioni

Conference proceedings (5)

Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Autori: Abdulmajid Murad, Massimiliano Ruocco
Pubblicato in: 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS), 2025
Editore: IEEE
DOI: 10.1109/ICNS65417.2025.10976929

Synthetic Flight Data Generation Using Generative Models

Autori: Karim Aly, Alexei Sharpanskykh
Pubblicato in: 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS), 2025
Editore: IEEE
DOI: 10.1109/ICNS65417.2025.10976960

Generation of Synthetic Aircraft Landing Trajectories Using Generative Adversarial Networks

Autori: Sebastiaan Wijnands, Alexei Sharpanskykh, Karim Aly
Pubblicato in: SESAR Innovation Days 2024, 2024
Editore: Zenodo / SESAR JU
DOI: 10.5281/ZENODO.14774663

Generating Synthetic Aircraft Trajectories

Autori: SINTEF Team
Pubblicato in: 2024
Editore: Zenodo / SESAR JU
DOI: 10.5281/ZENODO.14192164

Learning to Land Anywhere: Transferable Generative Models for Aircraft Trajectories

Autori: Olav Finne Præsteng Larsen, Massimiliano Ruocco, Michail Spitieris, Abdulmajid Murad, Martina Ragosta
Pubblicato in: SESAR Innovation Days 2025, 2025
Editore: Zenodo / SESAR JU
DOI: 10.5281/ZENODO.18186881

Peer reviewed articles (1)

Pre-tactical flight-delay and turnaround forecasting with synthetic aviation data

Autori: Abdulmajid Murad, Massimiliano Ruocco
Pubblicato in: CEAS Aeronautical Journal, 2026, ISSN 1869-5582
Editore: Springer Science and Business Media LLC
DOI: 10.1007/S13272-026-00941-7