Risultati finali
This deliverable define the target operational framework (requirements, scenarios and use cases) for the evaluation of effectiveness of ISOBAR solution.
Final Project Results ReportThis deliverable provides the ISOBAR findings which will include the description of final ISOBAR Solution a summary of proposed concepts and engines and the results with evidence of benefits and operational feasibility Furthermore technological risk and costs are analysed and regulatory implications are proposed together with a preliminary plan for the next RD phases
Multi-model probability of convection on a set of use casesThis deliverable provides probability of convection for a set of use cases focusing on the local and regional ATFCM needs
Applicable PF and evaluation referenceThis deliverable provides a description of the ATFCM Performance framework and the reference for evaluation corresponding to the identified use cases
ML demand prediction modelThis deliverable describes a function capable of estimating the demand fluctuations due to convective weather The deliverable will be updated at T016 to integrate further refinement of the model
Report on ISOBAR Evaluation and roadmap for ISOBAR B2B serviceThis deliverable describes the performance of ISOBAR solution against the baseline reference It includes the execution of the simulation campaign which covers the execution of ISOBAR solution service prototype to produce mitigation strategies tailored to scenarios for the selected use cases The report will elaborate recommendations for the next RD steps defining a highlevel roadmap for further development of ISOBAR service and integration in NM B2B services
Enhanced ATFCM Process and Service RequirementsIntegrated operational flow of ISOBAR processes and models that will guide the developments in WP2 WP3 and WP4 The deliverable will be updated at T022 to integrate the results of technical WPs including feedback from the evaluation of effectiveness and consolidate them into an enhanced ATFCM process incorporating convective weather information The deliverable update will also include final requirements from T13
This deliverable provides an experimental prototype with all ISOBAR modules integrated in and usable for evaluation through simulations The final HMI would be standalone designed to understand the concept and ease the dissemination of the project
Enhanced DCB algorithm with reinforcement learningThis deliverable provides an optimisation model for an enhanced DCB process The optimal coefficients will be computed using reinforcement learning based on the feedback in tactical ATFCM including an assessment of the relevant KPIs for each set of DCB solutions
Storm predictive modelThis deliverable addresses the development of an enhanced convection indicator capable of identifying locationseverity and time window of storms
This deliverable will develop a library to identify hotspots in the airspace system given the demand and capacity profiles developed and refined
Pubblicazioni
Autori:
C. Huang and Y. Xu
Pubblicato in:
2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, Pagina/e pp. 1-10
Editore:
IEEE
DOI:
10.1109/dasc52595.2021.9594397
Autori:
Y. Tang and Y. Xu
Pubblicato in:
2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), 2021, Pagina/e pp. 1-10
Editore:
IEEE
DOI:
10.1109/dasc52595.2021.9594329
Autori:
Khassiba, A., and Delahaye, D.
Pubblicato in:
12th SESAR Innovation Days, Numero 5-8 December SIDs 2022, 2022
Editore:
SESAR JU
Autori:
Aniel Jardines, Manuel Soler, Javier García-Heras, Matteo Ponzano, Laure Raynaud, Lucie Rottner, Juan Simarro, and Florenci Rey
Pubblicato in:
Conference Paper published at the General Assembly of the European Geoscience Union (EGU’21), Numero 19–30 Apr 2021, EGU21-7516, 2021
Editore:
EGU General Assembly 2021
DOI:
10.5194/egusphere-egu21-7516
Autori:
Ramón Dalmau, Gilles Gawinowski & Camille Anoraud
Pubblicato in:
12th SESAR Innovation Days, Numero 5-8 December SIDs 2022, 2022
Editore:
SESAR JU
Autori:
Aniel Jardines; Manuel Soler; Alejandro Cervantes; Javier García-Heras; Juan Simarro
Pubblicato in:
Machine Learning with Applications, Numero Machine Learning with Applications 5 (2021) 100053 (https://www.sciencedirect.com/science/article/pii/S2666827021000256?via%3Dihub), 2021, ISSN 2666-8270
Editore:
Elsevier
DOI:
10.1016/j.mlwa.2021.100053
Autori:
Yutong Chen; Minghua Hu; Yan Xu; Lei Yang
Pubblicato in:
Chinese Journal of Aeronautics, Numero 24 January 2023, 2023, ISSN 1000-9361
Editore:
Press of Acta Aeronautica et Astronautica Sinica
DOI:
10.1016/j.cja.2023.01.010
Autori:
Ramon Dalmau; Gilles Gawinowski; Camille Anoraud
Pubblicato in:
Journal of Air Transport Management, Numero October 2022, 2022, ISSN 1873-2089
Editore:
Elsevier
DOI:
10.1016/j.jairtraman.2022.102284
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