A machine learning model to predict runway exit at Vienna airport

Journal article (2019)

Authors

Floris Friso Herrema Air Transport & Operations - Aerospace Engineering
Sander Hartjes Air Transport & Operations - Aerospace Engineering
Mohamed Ellejmi EUROCONTROL
Steven Bancroft EUROCONTROL
Michael Schultz Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Research Group
Air Transport & Operations (Aerospace Engineering) (TU Delft)
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Published Date

01-11-2019

Language

English

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Faculty

Aerospace Engineering

Department

Control & Operations

Research Group

Air Transport & Operations

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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