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dc.contributor.advisorWilliam D. Hall and John-Paul Clarke.en_US
dc.contributor.authorVanderson, William W. (William Walter), 1977-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2007-02-21T11:23:43Z
dc.date.available2007-02-21T11:23:43Z
dc.date.copyright2000en_US
dc.date.issued2000en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/36102
dc.descriptionThesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.en_US
dc.descriptionIncludes bibliographical references (p. 88).en_US
dc.description.abstractIn this thesis, a forecasting model is described that improves departure predictions over those of Collaborative Decision Making (CDM), reducing error by up to 30% for a given day. This model propagates delay from incoming flights to outgoing flights by using minimum turn times calculated from Airline Service Quality Performance (ASQP) data. The model was run on data covering every day of March, April, and May of 1999, and produced departure predictions 6 hours, 4 hours, 2 hours, and 1 hour in advance of departure time. The greatest improvement on CDM predictions was achieved in the 4-hour predictions.en_US
dc.description.statementofresponsibilityby William W. Vanderson.en_US
dc.format.extent88 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleImproving aircraft departure time predictabilityen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc48271734en_US


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