FACET (F uture A ir Traffic Management C oncepts E valuation T ool) is a simulation and analysis tool being developed at the NASA Ames Research Center. This paper introduces the design, architecture, functionalities and applications of FACET. The purpose of FACET is to provide a simulation environment for exploration, development and evaluation of advanced Air Traffic Management concepts. Examples of these concepts include new Air Traffic Management paradigms such as Distributed Air/Ground Traffic Management, advanced Traffic Flow Management, and new Decision Support Tools for controllers working within the operational procedures of the existing air traffic control system. FACET models system-wide en route airspace operations over the contiguous United States. The architecture of FACET strikes an appropriate balance between flexibility and fidelity. This feature enables FACET to model airspace operations at the U.S. national level, and process over 5,000 aircraft on a single desktop computer running on any of a wide variety of operating systems. FACET has been designed with a modular software architecture to facilitate rapid prototyping of diverse Air Traffic Management concepts. FACET has prototypes of several advanced Air Traffic Management concepts: airborne self-separation; a Decision Support Tool for direct routing; advanced Traffic Flow Management techniques utilizing dynamic density predictions for airspace redesign and aircraft rerouting; and, the integration of space launch vehicle operations into the U.S. National Airspace System.

The increase in delays in the National Airspace System (NAS) has been the subject of several studies in recent years. These reports contain delay statistics over the entire NAS, along with some data specific to individual airports, however, a comprehensive characterization and comparison of the delay distributions is absent. Historical delay data for these airports are summarized. The various causal factors related to aircraft, airline operations, change of procedures and traffic volume are also discussed. Motivated by the desire to improve the accuracy of demand prediction in enroute sectors and at airports through probabilistic delay forecasting, this paper analyzes departure and arrival data for ten major airports in the United States that experience large volumes of traffic and significant delays. To enable such an analysis, several data fields for every aircraft departing from or arriving at these ten airports in a 21day period were extracted from the Post Operations Evaluation Tool (POET) database. Distributions that show the probability of a certain delay time for a given aircraft were created. These delay-time probability density functions were modeled using Normal and Poisson distributions with the mean and standard deviations derived from the raw data. The models were then improved by adjusting the mean and standard deviation values via a least squares method designed to minimize the fit error between the raw distribution and the model. It is shown that departure delay is better modeled using a Poisson distribution, while the enroute and arrival delays fit the Normal distribution better. Finally, correlation between the number of departures, number of arrivals and departure delays is examined from a time-series modeling perspective.

No abstract

No abstract

No abstract