In this thesis, I introduce a promising framework for representing an air traffic flow (stream) and flow-management action operating under weather uncertainty. I propose to use a meshed queuing and Markov-chain model---specifically, a queuing model whose service-rates are modulated by an underlying Markov chain describing weather-impact evolution---to capture traffic management in an uncertain environment. Two techniques for characterizing flow-management performance using the model are developed, namely 1) a master-Markov-chain representation technique that yields accurate results but at relatively high computational cost, and 2) a jump-linear system-based approximation that has promising scalability. The model formulation and two analysis techniques are illustrated with numerous examples. Based on this initial study, I believe that the interfaced weather-impact and traffic-flow model analyzed here holds promise to inform strategic flow contingency management in NextGen.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc68071 |
| Date | 05 1900 |
| Creators | Zhou, Yi |
| Contributors | Scharnberg, William, Wan, Yan, Fu, Shengli, Namuduri, Kamesh, Roy, Sandip, Wanke, Craig |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Copyright, Zhou, Yi, Copyright is held by the author, unless otherwise noted. All rights reserved. |
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