Neera P. Sood

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In the U.S. National Airspace System (NAS) a function called traffic flow management (TFM) seeks a balance between resource capacities and the demands placed upon them by air traffic. In general, capacity cannot be manipulated, and it is necessary for demand to be altered to meet a reduced capacity. Typically, demand can be altered in time (via delay, i.e.,(More)
This paper describes how to achieve a desired speedup by careful selection of appropriate algorithms for paralleliza-tion. Our target simulation is the Total Airport and Airspace Model (TAAM), a worldwide standard for aviation analysis. TAAM is designed as a sequential program, and we have increased its speed by incorporating multi-threaded algorithms with(More)
We would like to evaluate the XCS [1] Learning Classifier System (LCS [2]) to see if it can be applied to a specific aviation industry problem. We are interested in seeing whether it can offer an accessible representation model and evolve feasible strategies to predict future demand patterns endogenously, and in parallel with the supply side simulation.
This paper describes how to achieve a desired speedup by careful selection of appropriate algorithms for parallelization. Our target simulation is the Total Airport and Airspace Model (TAAM), a worldwide standard for aviation analysis. TAAM is designed as a sequential program, and we have increased its speed by incorporating multi-threaded algorithms with(More)
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