Forecasting airport transfer passenger flow using real-time data and machine learning

Problem definition: Airports and airlines have been challenged to improve decision-making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers' connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas.Academic/Practical relevance: Our work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport, using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment.Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system.Results: We show that when compared to benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas.Managerial implications: Our predictive system can produce accurate forecasts frequently and in realtime. With these forecasts, an airport's operating team can make data-driven decisions, identify late passengers and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted