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Identification of Robust Terminal-Area Routes in Convective WeatherConvective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic flow management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked. Given a flight route through the terminal area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast to be blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, whereas the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes
Document ID
20130005739
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Pfeil, Diana Michalek
(Massachusetts Inst. of Tech. Cambridge, MA, United States)
Balakrishnan, Hamsa
(Massachusetts Inst. of Tech. Cambridge, MA, United States)
Date Acquired
August 27, 2013
Publication Date
January 1, 2012
Publication Information
Publication: Transportation Science
Volume: 46
Issue: 1
ISSN: 0041-1655
Subject Category
Meteorology And Climatology
Funding Number(s)
CONTRACT_GRANT: NNA06CN24A
CONTRACT_GRANT: NSF ECCS-0745237
Distribution Limits
Public
Copyright
Other
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