Probabilistic Methods for Air Traffic Demand Forecasting

Abstract

Efficient and effective air-traffic management decisions can be dependent on the accuracy of demand predictions for sector loading and airport arrival rates. The accuracy of deterministic demand-forecasting methods in current use can only be improved by increasing the accuracy of the data used to make demand predictions. These demand-forecasting methods are deterministic because they utilize location predictions of individual aircraft as precise values for estimating demand. However, demand is based on the total number of aircraft utilizing a resource, not on which particular aircraft are included in the demand count. So probabilistic methods, that model how the demand utilization uncertainties of all aircraft will interact, should provide better demand estimates. The development of simple and efficient probabilistic methods for air-traffic demandforecasts is presented. Monte Carlo simulations demonstrate that the methods produce count errors for Airport Arrival Rate estimates with 15 to 20% lower standard deviations than those produced by deterministic methods. A reduction that is equivalent to what could be achieved deterministically by reducing the uncertainty in airport arrival-time predictions by 25 to 35%.