Layered Moment-Linear Network Models as Tools for Strategic Air Traffic Flow Management

For strategic traffic flow management, models are needed that allow NAS stakeholders to characterize the joint evolution of weather (and/or its impact on air traffic operating capabilities) in conjunction with the evolution of traffic flows. While queueing models show some promise for permitting meshed analysis of weather and traffic (including flow management initiatives), they are mathematically and computationally unwieldy when either the weather evolution or the airspace topology are modeled at realistic scale. Here, we investigate whether a new class of models known as layered moment-linear networks that approximate queueing-network models can serve as tractable albeit abstracted models for weather and traffic dynamics. As a first step toward constructing layered moment-linear models for traffic/weather, we develop a linear approximation for an M/D/1 queue with variable service rate. We evaluate the accuracy of the approximation using simulations, and then use it to analyze a traffic bottleneck that is modulated by a complex (networked) weather-propagation event.