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dc.contributor.authorBalakrishnan, Hamsa
dc.contributor.authorPfeil, Diana Michalek
dc.date.accessioned2013-09-23T18:16:47Z
dc.date.available2013-09-23T18:16:47Z
dc.date.issued2011-06
dc.date.submitted2011-01
dc.identifier.issn0041-1655
dc.identifier.issn1526-5447
dc.identifier.urihttp://hdl.handle.net/1721.1/80878
dc.description.abstractConvective 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 terminal-area 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.en_US
dc.description.sponsorshipUnited States. National Aeronautics and Space Administration (NGATS-ATM Airspace Program NNA06CN24A)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (ECCS-0745237)en_US
dc.language.isoen_US
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1287/trsc.1110.0372en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleIdentification of Robust Terminal-Area Routes in Convective Weatheren_US
dc.typeArticleen_US
dc.identifier.citationPfeil, D. M., and H. Balakrishnan. “Identification of Robust Terminal-Area Routes in Convective Weather.” Transportation Science 46, no. 1 (February 17, 2012): 56-73.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Divisionen_US
dc.contributor.mitauthorBalakrishnan, Hamsaen_US
dc.contributor.mitauthorPfeil, Diana Michaleken_US
dc.relation.journalTransportation Scienceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsPfeil, D. M.; Balakrishnan, H.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8624-7041
mit.licenseOPEN_ACCESS_POLICYen_US


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