  General real-time runway occupancy time prediction modelling for multiple
airports is a current research gap. An attempt to generalize a real-time
prediction model for Arrival Runway Occupancy Time (AROT) is presented in this
paper by substituting categorical features by their numerical equivalences.
Three days of data, collected from Saab Sensis' Aerobahn system at three US
airports, has been used for this work. Three tree-based machine learning
algorithms: Decision Tree, Random Forest and Gradient Boosting are used to
assess the generalizability of the model using numerical equivalent features.
We have shown that the model trained on numerical equivalent features not only
have performances at least on par with models trained on categorical features
but also can make predictions on unseen data from other airports.
