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 time-of-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.
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