A mathematical model for freeway incident detection and characterization: a fuzzy approach

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dc.contributor Fonseca, Daniel J.
dc.contributor Greene, Christopher
dc.contributor Moynihan, Gary P.
dc.contributor Rice, Richard L.
dc.contributor Schumacker, Randall E.
dc.contributor.advisor Rice, Margaret L.
dc.contributor.author Brumback, Terry Eugene
dc.date.accessioned 2017-02-28T22:22:25Z
dc.date.available 2017-02-28T22:22:25Z
dc.date.issued 2009
dc.identifier.other u0015_0000001_0000160
dc.identifier.other Brumback_alatus_0004D_10155
dc.identifier.uri https://ir.ua.edu/handle/123456789/666
dc.description Electronic Thesis or Dissertation
dc.description.abstract This research presents a fuzzy clustering methodology to automatic incident detection and characterization. The methodology is presented through an automated decision support system that incorporates a GeoMapping re-routing module in high priority incidents. The proposed methodology is then compared with similar methods of incident detection and characterization.
dc.format.extent 167 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Engineering, Civil
dc.subject.other Engineering, Industrial
dc.subject.other Engineering, System Science
dc.title A mathematical model for freeway incident detection and characterization: a fuzzy approach
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Dept. of Educational Leadership, Policy, and Technology Studies
etdms.degree.discipline Instructional Leadership
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.

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