A machine learning model to predict runway exit at Vienna airport

Journal Article (2019)
Author(s)

Floris Herrema (TU Delft - Air Transport & Operations)

Ricky Curran

S. Hartjes (TU Delft - Air Transport & Operations)

Mohamed Ellejmi (EUROCONTROL)

Steven Bancroft (EUROCONTROL)

Michael Schultz (Deutsches Zentrum für Luft- und Raumfahrt (DLR))

Research Group
Air Transport & Operations
DOI related publication
https://doi.org/10.1016/j.tre.2019.10.002
More Info
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Publication Year
2019
Language
English
Research Group
Air Transport & Operations
Volume number
131
Pages (from-to)
329-342

Abstract

Runway utilisation is a function of actual yearly runway throughput and annual capacity. The aim of the analysis in this project is to find data driven prediction models based on the features and relevant scenarios that might impact runway utilisation. The Gradient Boosting machine learning method will be assessed on their forecast performance and computational time for predicting the procedural and non-procedural runway exit to be utilised after the landing rollout. The Gradient Boosting method obtained an accuracy of 79% and was used to observe key related precursors of unique data patterns. Tests were conducted using runway and final approach data consisting of 54,679 arrival flights at Vienna airport.

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