We introduce a queuing network model that can comprehensively represent traffic flow dynamics and flow management capabilities in the U.S. National Airspace System (NAS). We envision this model as a framework for tractably evaluating and designing coordinated flow management capabilities at a multi-Center or even NAS-wide spatial scale and at a strategic (2-15 h) temporal horizon. As such, the queuing network model is expected to serve as a critical piece of a strategic flow contingency management solution for the Next Generation Air Traffic System (NextGen). Based on this perspective, we outline, in some detail, the evaluation and design tasks that can be performed using the model, as well as the construction of the flow network underlying the model. Finally, some examples are presented, including one example that replicates traffic in Atlanta Center on an actual bad-weather day, to illustrate simulation of the model and interpretation/use of model outputs.Index Terms-Flow contingency management (FCM), queuing network model, strategic air traffic management.

In this article, we introduce a promising framework for representing an air traffic flow (stream) and flow-management action operating under weather uncertainty. We propose to use a meshed queuing and Markov-chain model-specifically, a queuing model whose service-rates are modulated by an underlying Markov chain describing weather-impact evolution-to capture traffic management in an uncertain environment. Two techniques for characterizing flow-management performance using the model are developed, namely 1) a master-Markov-chain representation technique that yields accurate results but at relatively high computational cost, and 2) a jump-linear system-based approximation that has promising scalability. The model formulation and two analysis techniques are illustrated with numerous examples. Based on this initial study, we believe that the interfaced weather-impact and traffic-flow model analyzed here holds promise to inform strategic flow contingency management in NextGen.

Motivated by challenges in flow-contingency management, we introduce a stochastic network model for the spatiotemporal evolution of weather impact at a strategic time horizon. Specifically, we argue that a model that represents weather-impact propagation using local probabilistic influences can capture the rich dynamics and inherent variability in weather impact at the spatial and temporal resolution of interest. We then illustrate that such an influence model for weather impact is simple enough to permit a family of analyses that are needed for decision-support, including 1) model parameterization to meet probabilistic forecasts at time snapshots, 2) fast simulation of representative weather trajectories and impact probabilities, and 3) computation of correlations and higher-order statistics in weather impact. Also, lower-order representation of the stochastic dynamics at critical locations in the airspace is considered. Finally, a brief exploratory discussion is given on how the weather-impact model may eventually be used in tandem with network flow models to study flow contingency management.

This paper presents an operational concept and corresponding framework for flow contingency management, a component of strategic traffic flow management in the Next Generation Air Transportation System. The concept and framework described in this paper aim to address the lack of information, and simulation and evaluation capabilities provided to decision makers in today's strategic planning process. Specifically, the proposed concept explicitly models the uncertainties present at longer look-ahead times and provides quantitative analysis tools to evaluate the impact of proposed congestion-mitigation actions. This paper develops the overall concept and defines the associated modeling framework which specifies the flow of information throughout the decision making process. An example weather and traffic situation, taken from historic data, is simulated to illustrate the concept.

No abstract