Operations and Supply Chain Management Commons^™

Forecasting Airport Transfer Passenger Flow Using Realtime Data And Machine Learning, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

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 …