A new approach is developed for identifying and approximating well-traveled routes in a historical dataset of flight trajectories. The approximate routes are intended for use in a route-based Eulerian model of air traffic flow for strategic planning but are useful for other route-based strategic planning. The approach involves coarse clustering, outlier detection, fine clustering, and aggregate route construction. Coarse clustering is based on common origin, destination, and average cruise speed. Fine clustering, based on the Fréchet distance between pairs of trajectories, is applied to each coarse cluster to subdivide it, if appropriate. The coarse-clustering step reduces the number of trajectory pairs for which the Fréchet distance must be computed. The number of fine clusters is automatically determined using a combination of three performance indices. Outliers are identified using previously developed methods. The outliers could be discarded or assessed to identify potential routes for avoiding areas that are flight-constrained. The effectiveness of the approach for determining aggregate well-traveled routes is demonstrated on a historical dataset for a domain composed of six centers with a total of 19 airports.@en

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.@en

In response to severe weather conditions, Traffic Managers specify flow constraints and reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options and associated airspace capacities would assist Traffic Managers in making more efficient decisions in response to convective weather. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. This paper
describes the initial steps and methodology used towards this goal. The focus of this work is flights departing from Fort Worth Center destined for New York Center. Dominant routing structures used in the absence of convective weather are identified. A method to extract relevant features from the large volume of weather data available to quantify the impact of convective weather on this routing structure over a given time range is presented. Finally, a method of estimating flow rate capacity along commonly used routes during convective
weather events is described. Results show that the flow rates drop exponentially
as a function of the values of the proposed feature and that convective weather on the final third of the route was found to have a greater impact on the flow rate restriction than other portions of the route.@en