Explore the words cloud of the INTUIT project. It provides you a very rough idea of what is the project "INTUIT" about.
The following table provides information about the project.
Coordinator |
NOMMON SOLUTIONS AND TECHNOLOGIES SL
Organization address contact info |
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 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
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 |
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.
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.
year | authors and title | journal | last update |
---|---|---|---|
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 GarcÃa 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.