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Ongoing research is currently focused on the need to improve the strategic traffic flow management decision making processes. The research effort in this paper is part of a greater research initiative aimed at developing quantitative analysis and design capabilities for flow contingency management, which include the design of queuing network models. This paper proposes to use the concept of route assignment for managing aggregate air traffic demand and evaluating flow contingency plans generated by human decision makers. To determine how strategic traffic controls are captured and the computational requirements associated, a simulation experiment is conducted with realistic demand data and weather impact scenarios. System-wide performance results on a realistic plan of strategic control are analyzed, and the computational requirements of arc-based and route-based modeling choices will also be discussed.

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Motivated by needs in strategic AirTraffic Flow Management, we propose a model for forecasting airport arrival and departure capacity over a full-day look-ahead horizon. The focus of the modeling effort is on a small set of high-congestion airports, for which we propose a multi-stage prediction model. In this article, a core piece of the model -an operations-driven prediction for airport runway configurations and baseline capacities in terms of weather and operational regressor -is developed. We demonstrate the model's performance through case studies for two high-congestion airports, Chicago O'Hare International Airport (KORD) and Boston Logan International Airport (KBOS).

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