Data-Driven Modeling and Prediction of the Process for Selecting Runway Configurations

Abstract

Runway configuration is a key driver of airport capacity at any time. Several factors, such as wind speed and direction, visibility, traffic demand, air traffic controller workload, and the coordination of flows with neighboring airports, influence the selection of the runway configuration. This paper infers the utility functions of the nominal decision-making process of air traffic personnel by using a discrete choice modeling approach. Given operational and weather conditions that have already been reported, such as ceiling and visibility, traffic demand, and current runway configuration, the model produces a probabilistic forecast of the runway configuration on a 15-min horizon. The prediction is then extended to a more realistic 3-h planning horizon. Case studies for San Francisco (SFO), California; LaGuardia (LGA), New York; and Newark (EWR), New Jersey, airports were completed by using this approach. Given the weather and airport arrival demand, the model predicts the correct runway configuration at SFO, LGA, and EWR on a 3-h horizon with accuracies of 81.2%, 81.3%, and 77.8%, respectively.