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INTUIT SIGNED

Interactive Toolset for Understanding Trade-offs in ATM Performance

Total Cost €

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EC-Contrib. €

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Partnership

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Project "INTUIT" data sheet

The following table provides information about the project.

Coordinator
NOMMON SOLUTIONS AND TECHNOLOGIES SL 

Organization address
address: CALLE CLAUDIO COELLO 124 - PLANTA 4A TRASERA
city: MADRID
postcode: 28006
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website http://www.intuit-sesar.eu/
 Total cost 998˙125 €
 EC max contribution 998˙125 € (100%)
 Programme 1. H2020-EU.3.4.7.1 (Exploratory Research)
 Code Call H2020-SESAR-2015-1
 Funding Scheme SESAR-RIA
 Starting year 2016
 Duration (year-month-day) from 2016-03-01   to  2018-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    NOMMON SOLUTIONS AND TECHNOLOGIES SL ES (MADRID) coordinator 306˙312.00
2    FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. DE (MUNCHEN) participant 244˙000.00
3    ADVANCED LOGISTICS GROUP SAU ES (BARCELONA) participant 151˙437.00
4    TRANSPORT & MOBILITY LEUVEN NV BE (LEUVEN) participant 148˙187.00
5    UNIVERSIDAD POLITECNICA DE MADRID ES (MADRID) participant 148˙187.00

Map

 Project objective

ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions. Trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide KPIs.

The goal of INTUIT is to explore the potential of visual analytics, machine learning and systems modelling techniques to improve our understanding of the trade-offs between ATM KPAs, identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are:

1. to conduct a systematic characterisation of the ATM performance datasets available at different spatial and temporal scales and evaluate their potential to inform the development of new indicators and modelling approaches; 2. to propose new metrics and indicators providing new angles of analysis of ATM performance; 3. to develop a set of visual analytics and machine learning algorithms for the extraction of relevant and understandable patterns from ATM performance data; 4. to investigate new data-driven modelling techniques and evaluate their potential to provide new insights about cause-effect relationships between performance drivers and performance indicators; 5. to integrate the newly developed analytical and visualisation functionalities into an interactive dashboard supporting multi-dimensional performance assessment and decision making for both monitoring and management purposes.

 Deliverables

List of deliverables.
Performance Monitoring and Management Toolset Demonstrators, pilots, prototypes 2019-05-31 10:51:46
Final Project Results Report Documents, reports 2019-05-31 10:51:51
Performance Monitoring and Management Toolset Evaluation Report Documents, reports 2019-05-30 14:44:21
Performance Metrics and Predictive Models Documents, reports 2019-05-30 17:10:59
Visual Analytics Exploration of Performance Data Documents, reports 2019-05-30 14:28:04
Performance Data Inventory and Quality Assessment Documents, reports 2019-05-30 14:28:05
Project Website Websites, patent fillings, videos etc. 2019-05-30 14:28:06
Qualitative analysis of performance drivers and trade-offs Documents, reports 2019-05-30 14:28:10

Take a look to the deliverables list in detail:  detailed list of INTUIT deliverables.

 Publications

year authors and title journal last update
List of publications.
2018 INTUIT Consortium
INTUIT Project Summary
published pages: , ISSN: , DOI:
2019-06-13
2017 Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia
Clustering Trajectories by Relevant Parts for Air Traffic Analysis
published pages: 1-1, ISSN: 1077-2626, DOI: 10.1109/TVCG.2017.2744322
IEEE Transactions on Visualization and Computer Graphics Vol. PP, No. 99 2019-06-13
2017 Rodrigo Marcos, Oliva García Cantú Ros, Ricardo Herranz
Combining Visual Analytics and Machine Learning for Route Choice Prediction: Application to Pre-Tactical Traffic Forecast
published pages: , ISSN: , DOI:
Proceedings of the 7th SESAR Innovation Days November 2017 2019-06-13
2016 INTUIT Consortium
Understanding Trade-offs in ATM Performance: State-of-the-art and Future Challenges
published pages: , ISSN: , DOI:
March 2016 2019-06-13
2016 Rodrigo Marcos, David Toribio, Núria Alsina, Laia Garrigó, Natalia Andrienko, Gennady Andrienko, Luca Piovano, Thomas Blondiau and Ricardo Herranz
Visual Analytics and Machine Learning for Air Traffic Management Performance Modelling:Preliminary Findings of the INTUIT Project and Prospects for Future Research
published pages: , ISSN: , DOI:
Proceedings of the 6th SESAR Innovation Days November 2016 2019-06-13
2017 Gennady Andrienko, Natalia Andrienko, Wei Chen, Ross Maciejewski, Ye Zhao
Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions
published pages: 2232-2249, ISSN: 1524-9050, DOI: 10.1109/TITS.2017.2683539
IEEE Transactions on Intelligent Transportation Systems 18/8 2019-06-13

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The information about "INTUIT" are provided by the European Opendata Portal: CORDIS opendata.

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