Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data

TL;DR

This paper introduces machine learning decision support models to predict and explain missed passenger connections at airports across different planning stages, using complex data and providing high accuracy and interpretability.

Contribution

It presents novel models for predicting missed connections at various stages, employing advanced data balancing and explanation techniques for high-dimensional, noisy data.

Findings

Models achieve AUC > 0.93 across all horizons

Scheduled connection times are the most influential factors

Passenger age and border control requirements also significantly impact predictions

Abstract

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.