A machine learning model to predict runway exit at Vienna airport. (November 2019) - Herrema, Floris; Curran, Ricky; Hartjes, Sander; Ellejmi, Mohamed; Bancroft, Steven; Schultz, Michael
Highlights: The runway exit utilised is key in generating predictions with respect to the predicting runway throughput. The ML technique Random Forest that we use to build the model is fast, intuitive and interpretable. The model can help the airport managers to understand the driving features of the runway exit to be used. The model can update the predictions in real-time. 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.