Brendan P. HoganOffice: Former Reactor Building, Room 102, UVA Map H, #12Mailing Address: University of Virginia Department of Systems and Information Engineering P.O. Box 400747 151 Engineer's Way Charlottesville, VA 22904 Phone: 571-643-6299 (cell) 434-243-2280 (office) 434-982-2972 (fax) Email: bhogan@virginia.edu |
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The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is a NSF Engineering Research Center that uses a grid of short range, low powered weather radars to scan the lower region of the earth's troposhere that is not adequately covered by today's NEXRAD technology. Due to the curvature of the earth, the long range NEXRAD radars at their outer extent may miss tornadoes and other severe weather that develop in the lower elevations. The goal of the CASA project is to provide high-resolution distributed adaptive weather scanning in these cases. An example of the storm details that are visible with the CASA system (left) in comparison with the NEXRAD radars (right) is shown in the images below.
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I am interested in exploring alternative methods of capacity allocation for the handling of demand imbalances in the National Airspace System. In particular my focus is on the en-route environment and taking advantage of the inherent preferences users have in their decisions with respect to the routes in the airspace that they access and the times, altitudes, and equipment specific to their use of routes. The basic idea of congestion pricing is interesting, that users are charged in proportion to the cost externalities of the delay produced by their additional use of the system. The application of this theory may be less feasible in the en-route airspace domain, but the concept of dynamically charging users for the ways in which they access the system could be very effective. Specifically, a system of providing users incentives to behave in ways that are beneficial to the overall system good could be an effective technique for demand management. For example, the current tax structure that is linked to passengers transported and the fares they paid is a missed opportunity to incentivize good behavior since transporting the same number of passengers on smaller, more frequent aircraft generates more demand on the system but doesn't cost users any more to do it. Consider the alternative in which actions that increase the use of heavily demanded resources cost users proportionally more for that access, and incentives are provided to users who offload from those busy routes and times. This new line of thinking about traffic flow management is the motivation for my research.
As a first step along these lines I plan to explore the relevant datasets to identify patterns of activity that reveal users' underlying preference structures. For example, in the current implementation of ground delay programs carriers are free to swap their internal flights to best take advantage of the slots they are allocated when a particular resource is constrained. The actions that users take within this scope can reveal their business priorities with respect to which markets or which aircraft in their fleet are most important to be on time. Observing the actions that are taken along these lines can reveal the preference structure of different types of users. Once we have some insight into this information, we will be in a better position to recommend capacity allocation mechanisms that take advantage of those preferences, which is the ultimate goal of this work.
Some documents I have put together towards that end:
Maintained by bph4r@virginia.edu
Last Modified:
Monday, 20-Oct-2008 14:00:31 EDT