A network queuing model of the National Airspace System has been developed to support research into a strategic air traffic flow management capability. One of the challenges in the execution of the model is the size of the network - the computing resources required when modeling the entire United States are immense. As a way to reduce the network size, we investigate route clustering, i.e., grouping similar routes to reduce the number of paths between two airports. Clustering routes comes at a cost: as the number of clusters falls, the with-in cluster variability rises, and the solution quality is diminished. A trade-off curve for solution quality vs. cluster variability is developed for a sample problem involving seven major airports.
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