A machine learning model to predict runway exit at Vienna airport

Floris Herrema*, Ricky Curran, Sander Hartjes, Mohamed Ellejmi, Steven Bancroft, Michael Schultz

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)329-342
Number of pages14
JournalTransportation Research Part E: Logistics and Transportation Review
Publication statusPublished - 1 Nov 2019


  • Component
  • Gradient boosting
  • Runway capacity
  • Runway occupancy time
  • Runway utilisation


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