Combining Simulation Models and Big Data Analytics for ATM Performance Analysis

Informations projet

SIMBAD

N° de convention de subvention: 894241

DOI 10.3030/894241

Projet clôturé

Date de signature de la CE 21 Octobre 2020

Date de début 1 Janvier 2021

Date de fin 31 Decembre 2022

Financé au titre de

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Coût total € 999 937,50

Contribution de l’UE € 999 937,50

Coordonné par

NOMMON SOLUTIONS AND TECHNOLOGIES SL
Spain

Livrables

Documents, reports (7)

Active Learning Metamodelling

Document describing the metamodelling methodologies and querying strategies developed evaluating their performance in a test environment A first draft of D51 documenting the work performed in WP5 during the first reporting period will be available at T012

Specification of Case Studies

Document specifying the case studies and the required input data

Final Project Report

The Final Project Report will cover all the research activities performed by the project including the final publishable summary report the plan for use and dissemination of the foreground and a selfassessment of the TRL achieved at the end of the project This report will follow the template provided by the SJU and the guidelines provided in the SESAR Project Handbook

Methodologies and Algorithms for the Selection of Representative Traffic Samples

Technical report describing the new traffic pattern classifier and the proposed methodology for the selection of a representative set of traffic samples A first draft of D41 documenting the work performed in WP4 during the first reporting period will be available at T012

Data-Driven Methods for Trajectory Modelling

Technical report describing the new datadriven methods for the estimation of hidden variables and trajectory models and comparing the performance of the different proposed approaches A first draft of D31 documenting the work performed in WP3 during the first reporting period will be available at T012

Evaluation of the SIMBAD performance modelling framework and implementation guidelines

Document describing the results of the evaluation of the SIMBAD methodologies performed through the case studies and the guidelines on how to apply the new methods and tools to other simulation tools

Combining Simulation Models and Big Data Analytics for ATM Performance Analysis: Lessons Learnt from the SIMBAD Project and Way Forward

Lessons Learnt from the SIMBAD Project and Way Forward White paper providing a highlevel view of the main results and conclusions of the project

Websites, patent fillings, videos etc. (1)

Project Website

Public website including all the communication and dissemination material produced by the project.

Publications

Conference proceedings (3)

Data-Driven Estimation of Flights’ Hidden Parameters

Auteurs: Vouros, G., Tranos, T., Nlekas, K., Santipantakis, G., Melgosa, M., Prats, X.
Publié dans: Proceedings of the 12th SESAR Innovation Days, Numéro 5-8 December 2022, 2022
Éditeur: SESAR JU

Active learning metamodelling for R-NEST

Auteurs: Sanchez-Cauce, R., Riis, C., Antunes, F., Mocholí, D., Cantu Ros, O. G., Camara Pereira, F., Herranz, R., Lima Azevedo, C.
Publié dans: Proceedings of the 12th SESAR Innovation Days, Numéro 5-8 December 2022, 2022
Éditeur: SESAR JU

Identification of traffic patterns and selection of representative traffic samples for the assessment of ATM performance problems

Auteurs: Sánchez-Cauce, R., Mocholí, D., Cantú Ros, O. G., Herranz, R., Rodríguez, R., Tello, F., Fabio, A.
Publié dans: Proceedings of the 12th SESAR Innovation Days, Numéro 5-8 December 2022, 2022
Éditeur: SESAR JU

Other (1)

Lessons Learnt from the SIMBAD Project and Way Forward

Auteurs: Raquel Sánchez, David Mocholí, liva García Cantú, Ricardo Herranz, Rubén Rodríguez, George Vouros, Jordi Pons, Gennady Andrienko, Francisco Câmara Pereira, Francisco Antunes, Christoffer Riis
Publié dans: 2023
Éditeur: Nommon Solutions and Technologies SL