Visual Analytics and Machine Learning for Air Traffic Management Performance Modelling: Preliminary Findings of the INTUIT Project and Prospects for Future Research

Abstract: 

—INTUIT is a SESAR 2020 Exploratory Research project which aims to explore the potential of visual analytics and machine learning techniques to improve our understanding of the trade-offs between ATM KPAs and identify cause-effect relationships between indicators at different scales. The ultimate goal is to provide ATM stakeholders with new decision support tools for ATM performance monitoring and management. This paper introduces the project and reports its initial results. We propose a set of research questions on ATM performance identified through a combination of desk research and consultation with ATM stakeholders, we assess the main data sources on ATM performance available at European level, and we map the research questions previously defined to the data sources that are most relevant for each question. To illustrate the role that visual analytics can play in addressing these questions, we present the preliminary results of an ongoing case study focused on analysing the spatio-temporal patterns of ATFM delays in the European network. We finish by outlining future research directions.


Publication type: 
Congress
Published in: 
Proceedings of SESAR Innovation Days (SIDs) 2016
Project: 
Publication date: 
November 2016
CeDInt Authors: 
Other Authors: 
Rodrigo Marcos, David Toribio, Laia Garrigó, Nuria Alsina, Gennady Andrienko, Natalia Andrienko, Thomas Blondiau, Ricardo Herranz