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 that take into consideration the stochastic nature of weather. The main objective of this paper is the development of probabilistic capacity forecasts. To assess the improvements that could be gained by using the capacity probabilistic forecasts, the capacity distributions developed in this paper are input into existing static, stochastic, ground holding models, which uses probabilistic capacity forecasts and determines the amount of ground delay to assign to incoming flights.

Scheduling aircraft landings using airlines’ preferences

“…Consequently, these delays can be absorbed on the ground before departure rather than en-route. According to Inniss and Ball [15] it is about twice as cheap to delay an aircraft on the ground instead of in the air. The assignment of ground delays in case of arrival capacity shortage is often referred to as the single airport ground holding problem in the literature.…”

Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle

“…Another limitation of the dynamic models, as acknowledged by Mukherjee and Hansen (2007), is that the decisions made based on a particular set of scenarios, provided as input, may no longer be optimal if the set of possible AAR profiles (i.e., scenarios) themselves change with time. Nevertheless, the dynamic models show significant improvement over the static models , Inniss and Ball 2004, Richetta and Odoni 1993 by adapting updated capacity forecasts into the decision making process. In light of the shortcomings of the scenario-based planning models, Liu and Hansen (2007) proposed a scenario-free sequential decision making problem, based on dynamic programming techniques, for the stochastic SAGHP.…”

“…Scenarios and their probabilities can be constructed from weather forecasts and/or by analyzing historical AAR evolution at an airport, e.g. see (Inniss and Ball 2004), (Liu et al 2006) and (Wilson 2004). Ball et al (2003) proposed an aggregate version of the Richetta and Odoni model that directly sets the PAAR without assigning delays to specific flights.…”

A General Approach to Equity in Traffic Flow Management and Its Application to Mitigating Exemption Bias in Ground Delay Programs

“…We take as a given in this paper the need to restrict the geographical scope of a GDP and that TFM exercises this option at their discretion on a program-by-program basis (see [ 5], [ 6], [ 7] for a treatment of stochastic planning issues in GDPs). Instead, our interest lies in the ramifications of the exemptions.…”