  Predicting if passengers in a connecting flight will lose their connection is
paramount for airline profitability. We present novel machine learning-based
decision support models for the different stages of connection flight
management, namely for strategic, pre-tactical, tactical and post-operations.
We predict missed flight connections in an airline's hub airport using
historical data on flights and passengers, and analyse the factors that
contribute additively to the predicted outcome for each decision horizon. Our
data is high-dimensional, heterogeneous, imbalanced and noisy, and does not
inform about passenger arrival/departure transit time. We employ probabilistic
encoding of categorical classes, data balancing with Gaussian Mixture Models,
and boosting. For all planning horizons, our models attain an AUC of the ROC
higher than 0.93. SHAP value explanations of our models indicate that
scheduled/perceived connection times contribute the most to the prediction,
followed by passenger age and whether border controls are required.
