Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Identification of Air Traffic Flow Segments via Incremental Deterministic Annealing Clustering
    (2012) Nguyen, Alex T; Barras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    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.
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    DETERMINING THE RELATIONSHIPS AMONG AIRPORT OPERATIONAL PERFORMANCE AREAS AND OTHER AIRPORT CHARACTERISTICS
    (2009) Chan, Kennis Yuen Man; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this thesis, a methodology is proposed to investigate pair-wise relationships between different types of airport operational performance variables. The methodology represents a fundamental contribution for comparing airport performance between different air traffic management systems. Considerable attention is paid to analyzing the most appropriate techniques in an effort to produce the most reliable results. Additionally, a method to display the results in a simple and clear way is also suggested to allow users to understand the results visually. The key variables obtained from the proposed methodology not only serve as building blocks for developing models to answer a variety of air traffic questions, which allow policy makers to make decisions on allocating resources wisely, but also can be used as an evaluation tool to assist the FAA in selecting candidate projects.