Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data
This work addresses airline profitability by improving decision support for managing connecting flights, though it is incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the problem of predicting missed flight connections at an airline's hub airport using historical data, achieving an AUC higher than 0.93 across all planning horizons and identifying key factors like connection times and passenger age through SHAP explanations.
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.