A collaboration between Raleigh-Durham International Airport and SAS has led to the development of significantly improved passenger forecast models. Airport leaders are seeking alternative solutions because industry forecasts have become increasingly unreliable due to the unprecedented impact of the global health pandemic on air travel. Traditional forecasting methods accurately predicted passenger volume within a few points of actual traffic, but they have become varied and inconsistent amid the global health crisis.
“Airports cannot succeed without accurate projections about how many passengers will board a flight tomorrow or a year from now,” said Michael Landguth, president & CEO of the Raleigh-Durham Airport Authority. “RDU’s collaboration with SAS allows the airport to develop more precise forecasts that drive important business and planning decisions. We value this local partnership and are fortunate SAS offered to consult with the airport during this challenging period for the aviation industry.”
SAS, based in Cary, is one of the world’s premier analytics and forecasting companies. As a commitment to supporting the region in the wake of the COVID-19 crisis, the company’s analytics experts took on the task of evaluating RDU’s forecasting approach.
“As the pandemic continues, ongoing uncertainly has exposed current forecasting models’ limitations and the need for new approaches,” said Anthony Mancuso, Director of the Global Risk Advisory Consulting at SAS. “By applying the same methodologies that are helping our financial services customers weather the pandemic, SAS advised RDU on how to revise air traffic projections that tighten model confidence and better predict passenger volumes in the weeks and months ahead.”
SAS’ contributions to RDU’s efforts were felt immediately and continue to be a critical component in developing passenger forecast models during the COVID-19 pandemic. SAS’ analytics team developed an approach the airport could not have produced on its own or through working solely with airport consultants. The model developed in collaboration with SAS has predicted October passenger traffic within two points of expected traffic based on TSA screened passenger data. This tremendous success in forecasting during a period of heavy uncertainty would not have been possible without the expertise of the team at SAS.