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Data-driven AiRcraft Trajectory prediction research


Project results final report

This is the final report for DART.

Dissemination Report

This report specifies the DART dissemination achievements, supporting communication with targeted audiences, communities and businesses by developing information material, articles and reports on project achievements, using different kinds of communication strategies and mediums. The first version of this report will be in M 12 and updated regularly until M 24.

Dissemination Plan

The dissemination plan states the dissemination and exploitation goals of the project as well the means for achieving these goals. DART will underpin all of its dissemination actions with the desired needs of the identified target audiences, project results and the impact of past dissemination efforts. The dissemination plan and any dissemination collateral (website, posters etc.) should be considered as ‘living deliverables’ and should be shaped to complement the exploitation of project results during the lifetime of DART. The most relevant dissemination means that will be adopted are,technical reports,articles in journals and conference proceedings, and presentation of the project outcomes specially focused on the benefits of the use of the data-driven methods for the ATM community.The dissemination and exploitation plan will also include contact with stakeholders to support the prototype implementation and get a feedback from end-users.

Project website, wiki, social media channels

Social media platforms and project website that comprise this deliverable. These will gather and disseminate all the information related to DART. They will facilitate direct feedback and discussions among partners, as well with experts or other parties.

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DART: A Machine-Learning Approach to Trajectory Prediction and Demand-Capacity Balancing

Author(s): Esther Calvo Fernández, José Manuel Cordero, George Vouros, Nikos Pelekis, Theocharis Kravaris, Harris Georgiou, Georg Fuchs, Natalya Andrienko, Gennady Andrienko, Enrique Casado, David Scarlatti, Pablo Costas,Samet Ayhan
Published in: SESAR Innovation Days 2017, 2017

Data-driven Aircraft Trajectory Predictions using Ensemble Meta-Estimators

Author(s): E. Casado, A. Muñoz
Published in: DASC 2018, 2018

Multiagent Reinforcement Learning Methods for Resolving Demand-Capacity Imbalances

Author(s): H. Kravaris, C.Spatharis, K.Blekas, G.Vouros, J-M Cordero
Published in: DASC 2018, 2018

Multiagent Reinforcement Learning Methods to Resolve Demand Capacity Balance Problems

Author(s): Christos Spatharis, Theocharis Kravaris, George A. Vouros, Konstantinos Blekas, Georgios Chalkiadakis, Jose Manuel Cordero Garcia, Esther Calvo Fernandez
Published in: Proceedings of the 10th Hellenic Conference on Artificial Intelligence - SETN '18, 2018, Page(s) 1-9
DOI: 10.1145/3200947.3201010

Learning Policies for Resolving Demand--‐Capacity Imbalances during Pre--tactical Air Traffic Management

Author(s): T.Kravaris, G.Vouros, C.Spatharis, K.Blekas, G.Chalkiadakis, J--‐M.Cordero Garcia
Published in: 15th German Conference on Multiagent System Technologies, 2017

Integration of meteorological information in trajectory prediction (DART Project)

Author(s): J.M. Cordero García
Published in: 1st “International Workshop on Meteorology and Air Traffic Management, 2017

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Author(s): G Andrienko, N Andrienko, W Chen, R Maciejewski, Y Zhao
Published in: IEEE Transactions on Intelligent Transportation Systems, 2017, ISSN 1524-9050

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Author(s): Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia
Published in: IEEE Transactions on Visualization and Computer Graphics, 2017, ISSN 1077-2626