Latest revision as of 17:34, 3 February 2021

Abstract

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.

Original document

The different versions of the original document can be found in:

• http://pdfs.semanticscholar.org/22cc/0de3454569fad89cfe4ee130daf0cf5e6e83.pdf

• http://arc.aiaa.org/doi/pdf/10.2514/6.2012-4485,

http://dx.doi.org/10.2514/6.2012-4485

• https://arc.aiaa.org/doi/pdf/10.2514/6.2012-4485,

https://www.researchgate.net/profile/Craig_Wanke/publication/268005434_Modeling_Air_Traffic_Demand_for_a_Real-Time_Queuing_Network_Model_of_the_National_Airspace_System/links/546f38c00cf24af340c07ae1.pdf,

https://academic.microsoft.com/#/detail/2114589343