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A predictive model for departure traffic demand and its route distribution at look-ahead times of 2-15 hours is proposed, for use in a queuing-network-based tool for strategic Traffic Flow Management (TFM). The proposed model uses a combination of operational data (filed flight plans, schedules), historical statistics of demand, and timeof-operation-specific factors to generate statistical predictions of traffic demand for particular routes between pairs of airports or airport clusters. Specifically, a two-stage predictor for demand is proposed. First, traffic demand for an origin-destination (O-D) pair is modeled as the summation of a known demand which captures filed and scheduled traffic, and an unknown demand which is modeled as non-homogeneous Poisson process. Second, the fraction of this O-D traffic demand on each route is modeled using a linear regression, with the historical route fractions, known (filed) route fractions, and wind-adjusted transit times for the routes serving as regressors. Historical data on demands and actual traffic volumes are used to evaluate aspects of the model, including the Poisson-process assumption and the regression model for route distributions.

This paper describes a network modeling approach developed to support Flow Contingency Management, a component of the strategic traffic flow management system in the Next Generation Air Transportation System. The overall concept and associated modeling framework described in this paper provide a set of requirements for defining the network structure. Specifically, the network must be designed to allow a queuing model to propagate stochastic flows and analyze the impact of flow constraints as well as demandshaping controls. In addition, the network topology must result in a computationallytractable framework to support strategic timeframe decision making. To address these needs, a network model that uses multiple levels of resolution to represent various National Airspace System resources is proposed. Specifically, it is proposed that a boundary forming an area(s) of interest be defined, within which resources are represented at a greater level of detail than resources outside the area(s). Finally, an example problem, based on historic traffic and weather, is used to validate the effectiveness of using multiple levels of resolution within the network model and analyze the benefits and costs associated with proposing various boundaries on the area of interest.

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