An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Aggregate routes are created from flight trajectory data based on similarity measures. Spatial similarity is determined using the Fréchet distance and temporal similarity by comparing average ground speeds. The aggregate routes approximate actual traffic patterns. By specifying the model resolution, an appropriate balance between model accuracy and model dimension can be achieved. The dynamics of the traffic flow on the network of aggregate routes take the form of a discrete-time linear time-invariant system. The traffic flow controls are ground holding and predeparture rerouting. Strategic planning, to use the controls to modify the future traffic flow when local capacity violations are anticipated, is posed as an integer linear programming problem of minimizing a weighted sum of flight delays subject to capacity constraints. Two examples demonstrate the model formulation and results of strategic planning. First, ground delays are introduced to manage high demand in the Los Angeles center; second, ground holding and predeparture rerouting are used to manage a convective weather scenario in the same center.

An approach for identifying and approximating well-traveled routes in a historical dataset of flight trajectories is presented. The intent is to use these routes to construct a network model of air traffic flow for use in strategic planning. The flight trajectories are clustered based on spatial and dynamic similarity measures. A coarse clustering process first groups trajectories using origin, destination, and average ground speed information; then a fine clustering process uses the Fréchet distance between pairs of trajectories in the coarse clusters as the more accurate measure of spatial similarity to further divide the trajectories into smaller clusters. The number of resulting clusters is automatically determined by employing a combination of three performance indices. This two-step clustering process has two benefits. It reduces the computational burden because the Fréchet distance computations are required for fewer trajectory pairs due to the initial coarse clustering step. Secondly no manual tuning is required to determine the final number of clusters. A method of detecting and categorizing outliers is presented which fits well with the clustering
process. The clustering and outlier processes are demonstrated on a historical dataset for a region composed of 6 Centers with 21 airports.