Seasonal Forecasting Model to Determine the Loss of Passengers Traveling Through Heathrow Airport Due to COVID-19

ABSTRACT

Business, industry, and science all require data-driven decision-making. Decision-making and operation management require not only historic data but predictive data as well. Understanding the results delivered by applications of predictive analytics will help us prepare our business and operations not only qualitatively but quantitatively as well. To determine how passenger traffic at Heathrow Airport, London, was affected due to COVID-19, we have used the SARIMA model (using Python) to predict passenger traffic that should have been passing through the airport in between the months of January 2020 and August 2021 had the pandemic not struck the world. We aim to compare how the observed data vary from the predicted data and come up with possible routes to utilize the information gained from such a study. This predictive model will help in making operational decisions with much more ease and certainty, help to understand lost revenue, and cater to a case study that will help build better predictive models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.