Corpus ID: 28982381

Visual Analytics and Machine Learning for Air Traffic Management Performance Modelling

@inproceedings{Toribio2016VisualAA,
  title={Visual Analytics and Machine Learning for Air Traffic Management Performance Modelling},
  author={D. Toribio and R. Herranz and Laia Garrig{\'o} and N. Alsina and N. Adrienko and L. Piovano},
  year={2016}
}
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… Expand

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References

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Air traffic management performance assessment using flight inefficiency metrics
Framework to Assess an Area Control Centre's Operating Cost-efficiency: a Case Study
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