Estimating One-Parameter Airport Arrival Capacity Distributions for Air Traffic Flow Management

Abstract

During instances of capacity-demand imbalances, efficient planning and decision-making in air traffic flow management is contingent upon the “goodness” of the capacity distributions that estimate airport capacity over time. Airport capacities are subject to substantial uncertainty as they depend on stochastic weather conditions. In this paper, we develop models in the form of probabilistic capacity forecasts, which take into consideration the stochastic nature of weather. To assess the improvements that could be gained by using the probabilistic capacity forecasts, the seasonal capacity distributions developed in this paper for San Francisco’s International airport (SFO) are input into an existing static, stochastic, ground holding model, which uses probabilistic capacity forecasts and determines the amount of ground delay to assign to incoming flights. Experimental results show an 8.6% reduction in expected delay minutes.