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dc.contributor.advisorMatisziw, Timothy C. (Timothy Clark)eng
dc.contributor.authorDrum, David K.eng
dc.coverage.spatialUnited Stateseng
dc.date.issued2014eng
dc.date.submitted2014 Springeng
dc.description.abstractTraffic congestion is a daily occurrence in urban highway networks worldwide. It is not possible, however, for society to build its way out of congestion; rather, smarter roads and vehicles are needed. While the development of a smarter transportation system is underway, full implementation is years or decades from now. Yet, some of the sensing technology needed for smarter vehicles is already widely deployed in the form of smart phones. This thesis develops a novel method for recognizing traffic congestion using an artificially intelligent heuristic that could be implemented in a smart phone application or embedded system. Its goal is to provide intelligent feedback to a driver or autonomous vehicle control system to counteract stop-and-go traffic, a defining feature of urban highway congestion. Evaluation of the method indicates that a specific condition during stop-and-go traffic can be recognized accurately. A driver or control system acting upon feedback provided by the artificially intelligent system can improve traffic flow on the roadway by 1% to 3.5% over the course of the test duration.eng
dc.format.extent1 online resource (x, 119 pages) : illustrations, maps + 2 supplementary files.eng
dc.identifier.merlinb109690102eng
dc.identifier.oclc917631391eng
dc.identifier.urihttps://hdl.handle.net/10355/44263
dc.identifier.urihttps://doi.org/10.32469/10355/44263eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.publisher[University of Missouri--Columbia]eng
dc.relation.ispartofcollectionUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. These. 2014 Theses. 2014 Freely available theseseng
dc.subjectAuthor supplied: driver behavior, machine learning, decision-support systems, traffic congestion, driver feedback, real-time dataeng
dc.subject.lcshTraffic congestion -- Managementeng
dc.subject.lcshArtificial intelligenceeng
dc.titleCounteracting traffic congestion using intelligent driver feedbackeng
dc.typeThesiseng
thesis.degree.disciplineCivil engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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