The value of reduced uncertainty in air traffic flow management
Given the numerous sources of uncertainty inherent in the National Airspace System, plans
to alleviate demand-capacity imbalances are sometimes untrustworthy. In addition, differing
solutions to demand-capacity imbalances are difficult to compare in terms of their potential
quality in the face of weather and schedule uncertainties. In this work, a method for
evaluating the robustness of a traffic scheduling solution to various uncertainties is
presented. By converting solutions to predicted demands and capacities of sectors and …
to alleviate demand-capacity imbalances are sometimes untrustworthy. In addition, differing
solutions to demand-capacity imbalances are difficult to compare in terms of their potential
quality in the face of weather and schedule uncertainties. In this work, a method for
evaluating the robustness of a traffic scheduling solution to various uncertainties is
presented. By converting solutions to predicted demands and capacities of sectors and …
Given the numerous sources of uncertainty inherent in the National Airspace System, plans to alleviate demand-capacity imbalances are sometimes untrustworthy. In addition, differing solutions to demand-capacity imbalances are difficult to compare in terms of their potential quality in the face of weather and schedule uncertainties. In this work, a method for evaluating the robustness of a traffic scheduling solution to various uncertainties is presented. By converting solutions to predicted demands and capacities of sectors and airports together with measures of uncertainty on those predictions, any given plan can be evaluated based on the number of expected capacity violations along with distributions on their severity. With such measures, the most robust of a set of potential plans might be chosen by a traffic manager. As a side-effect of this approach, the value of reduced uncertainties can be measured in terms of reduced expected violations. The value of reduced uncertainty is demonstrated using a deterministic, minimum-delay approach to managing demand-capacity imbalances. When the deterministic solution is measured in terms of expected violations given models of demand and capacity uncertainty, results indicate that if the calculated uncertainty measures are reduced by 50%, then the number of expected violations will decrease by just over 40%.
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