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dc.contributor.advisorMegahed, Fadelen_US
dc.contributor.authorTsai, Yao Teen_US
dc.date.accessioned2015-12-10T20:31:28Z
dc.date.available2015-12-10T20:31:28Z
dc.date.issued2015-12-10
dc.identifier.urihttp://hdl.handle.net/10415/4955
dc.description.abstractHighway safety remains one the most critical issues in the United States. In recent years, new safety programs and policies have been adopted to save more lives, prevent tragedies, and reduce economic loss. Unfortunately, there are a limited number of surveillance methods for collecting, measuring and analyzing highway safety data. The correlation between research outcomes and stakeholder expectations must be strengthened. The objective of this dissertation is to help decision makers gain a better understanding of the impact of traffic policies, so that they can optimize their use of resources. Specifically, this work: 1) provides a visual data-mining toolkit for policy makers to uncover hidden information, monitor spatiotemporal issues, and determine the impact of policy changes; 2) investigates the complex relationships among socioeconomic factors, the public policies adopted, and fatality rates; and 3) provides information to assist decision makers in monitoring safety performance trends while reducing waste of resources. Overall, the research goal is to identify important factors that can facilitate the generation of a new vision for safety surveillance.en_US
dc.subjectIndustrial and Systems Engineeringen_US
dc.titleTowards the Identification of Predictor Variables for Highway Safetyen_US
dc.typeDissertationen_US
dc.embargo.statusNOT_EMBARGOEDen_US
dc.contributor.committeeSwartz, Stephenen_US
dc.contributor.committeeEvans, Johnen_US
dc.contributor.committeeSesek, Richarden_US


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