Flight deck crew reserve: From data to forecasting

We devise a supervised learning method, based on decision trees, to predict total monthly pilot reserve hours. The proposed method uses characteristics of the monthly schedule as factors and an iterative algorithm to make a prediction. The model is tested on real data and a substantial improvement is observed in the results compared to the current state of the practice in the domain in which a seasonal linear trend decomposition is employed.