Traffic flow management (TFM) in the U.S. is the process by which the Federal Aviation Administration (FAA), with the participation of airspace users, seeks to balance the capacity of airspace and airport resources with the demand for these resources. This is a difficult process, complicated by the presence of severe weather or unusually high demand. TFM in en-route airspace is concerned with managing airspace demand, specifically the number of flights handled by air traffic control (ATC) sectors; a sector is the volume of airspace managed by an air traffic controller or controller team. Therefore, effective decision-making requires accurate sector demand predictions. While it is commonly accepted that the sector demand predictions used by current and proposed TFM decision support systems contain significant uncertainty, this uncertainty is typically not quantified or taken into account in any meaningful way. The work described here is focused on measuring the uncertainty in sector demand predictions under current operational conditions, and on applying those measurements towards improving the performance and human factors of TFM decision support systems.

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Network queueing models, including some models of air traffic, exhibit sensitivity to very small changes in the times of events that are nominally simultaneous, such as flight departures (pushbacks). Such models might exhibit chaotic behavior when the ordering of otherwise simultaneous (or closely spaced) departures are changed. This paper explores the question as to whether such models do, indeed, exhibit chaotic behavior, or whether the effect is bounded. study of delays in National Airspace System (NAS) simulations. The study consisted of 32 replications of a NAS scenario, in which scheduled departures were given a small pseudorandom pushback delay, independent across replications and across airports. The simulated scenario was a hypothetical one configured for a generally good weather day. The standard deviation of total (viz., 59-airport) at-gate delays is reported; it exceeds the simulated reduction in delay that would be produced by increasing arrival capacity by ten percent at any airport, except for the five most-congested airports. We further analyze the results to determine whether chaotic effects are manifest in the mathematical model. This question is explored using an empirical

The MITRE Corporation conducted a study to explore methods for evaluating proposed National Airspace System (NAS) Architecture options.The study investigated analytic approaches for quantifying characteristics of candidate NAS architectures such as costs, benefits, and risks. One vision of the product of such an approach was a Consumer Reports-style comparison of candidate NAS Architecture options. Identifying the metrics to quantify was a major issue addressed in the study, which produced a list of metrics to be considered in NAS Architecture assessment and a process that could be used to coordinate and formalize assessment. The study also explored probabilistic modeling to predict values for some metrics and represent the uncertainties in the predictions. A new commercial software package, Analytica, was used for this activity. This paper describes the proposed metrics, a process for formalizing the assessment of metrics, and a probabilistic model of NAS safety that was developed to assess the potential of probabilistic modeling in NAS Architecture assessment.

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