Identification of Air Traffic Flow Segments via Incremental Deterministic Annealing Clustering

Many of the traffic management decisions and initiatives in air traffic are based on "flows" of traffic in the National Airspace System (NAS), but the actual identification of the location and time of the flow segments are often left to interpretation based on observations of traffic data points over time. Having an automated method of identifying major flow segments can help to target traffic management initiatives, evaluate design of airspace, and enable actions to be taken on the collection of flights in a flow segment rather than on the flights individually. A novel approach is developed to identify the major flow segments of air traffic in the NAS that consists of a robust method for partitioning 4-dimensional traffic trajectories into a series of great circle segments, and clustering the segments using an Agglomerate Deterministic Annealing clustering algorithm. In addition, a very efficient algorithm to incrementally cluster the segments is developed that takes into account the spatial and temporal properties of the segments, and makes the method very suitable for real-time applications. Further, an enhancement to the algorithm is provided that requires only a small subset of the segments to be clustered, drastically reducing the run time. Results of the clustering technique are shown, highlighting various major traffic flow patterns in the NAS. In addition, organizing the traffic into the flow segments identified using the Incremental Clustering method is shown to have a potential reduction in the number of conflict points. An application of the flow information is presented in the form of a Decision Support Tool (DST) that aids traffic managers in establishing and managing Airspace Flow Programs. In addition, the flow segment information is applied to a low-level form of aggregated traffic management, showing that aggregating flights into the flow segments and rerouting the whole flow segment can be efficiently performed as compared to rerouting individual aircraft separately, and can reduce the number of conflict points. Considerations for implementing these techniques in real-time systems are also discussed

An Axiomatic Approach To Clustering Line-segments

In this paper we consider the problem of clustering line-segments into new ones. The clustering-hierarchy gives an answer to the question what original line segments are combined into larger ones. Such a clustering is defined as a hierarchical ordering of a set of linesegments. Criteria on a clustering-method are presented. The difference between edges and lines in relation to scaleinvariant clustering is demonstrated. Existing approaches are evaluated using the presented criteria. It is shown that these approaches do not meet desirable criteria such as scale-invariance. A new method is described that adheres the formulated criteria. Finally an experiment is presented that illustrates the usefulness of the new method. 1 Introduction In computer vision, the detection of lines is an important and longstanding topic. Many authors, for example [Burns86], have described methods for detecting straight lines in grey-value images. In this paper we consider the problem of clustering straight..