Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

by Xiaojia Guo, Yael Grushka-Cockayne, and Bert De Reyck

Overview — Passengers arriving at international hubs often endure delays, especially at immigration and security. This study of London’s Heathrow Airport develops a system to provide real-time information about transfer passengers’ journeys through the airport to better serve passengers, airlines, and their employees. It shows how advanced machine learning could be accessible to managers.

Author Abstract

Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces quantile forecasts for the number of passengers arriving at the immigration and security areas. Academic/Practical relevance: Airports and airlines have been challenged to improve decision-making by producing accurate forecasts in real time. Our work is the first to apply machine learning for predicting real-time quantile forecasts in the airport. We focus on passengers’ connecting journeys, which have only been studied by few researchers. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive model developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict complete distributions, moving beyond point forecasts. To derive insights from the tree, we introduce the concept of a stable tree that can be summarized by its key variables’ splits. Results: We identify seven key factors that impact passengers’ connection times, dividing passengers into 16 passenger segments. We find that adding correlations among the connection times of passengers arriving on the same flight can improve the forecasts of arrivals at the immigration and security areas. When compared to several benchmarks, our model is shown to be more accurate in both point forecasting and quantile forecasting. Managerial implications: Our predictive system can produce accurate forecasts, frequently, and in real-time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late connecting passengers and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger arrivals. Our approach can be generalized to other domains, such as rail or hospital passenger flow.