A machine learning model to predict runway exit at Vienna airport

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

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 131, issue C, 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.

Keywords: Component; Runway utilisation; Runway capacity; Runway occupancy time; Gradient boosting (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.tre.2019.10.002

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