A macro-level analysis of safety data using geospatial techniques and spatial econometric methods and models

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dc.contributorLindly, Jay K.
dc.contributorHainen, Alexander M.
dc.contributorWeber, Joe
dc.contributorSmith, Randy K.
dc.contributor.advisorJones, Steven L.
dc.contributor.authorZephaniah, Samwel Oyier
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2018-06-04T14:57:17Z
dc.date.available2018-06-04T14:57:17Z
dc.date.issued2017
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractMotor vehicle accidents are a source of many preventable injuries and deaths, worldwide. Several statistical and econometric models have been developed to predict and explain crash events. Research indicate that 93% of traffic accidents are due to human error. The objective of this research is twofold – first, to develop a macro level safety planning framework by identifying socioeconomic factors that influence crash frequencies and second, to characterize traffic congestion attributed to a crash events. To this effect, a Geographically Weighted Poisson Regression (GWPR) model, a suite of Spatial Econometric models and a Mixed Logit model were estimated. Data used included crash records from 2009 to 2013 in Alabama comprising 647,477 crash events. These included 4,814 crashes on Interstate 65 and 21,818 crashes related to Driving Under the Influence (DUI). Other data comprised socioeconomic data from US census, weather data, traffic data, spatial data from ESRI and crowd sourced speed data. Results indicate that DUI crash rates and frequencies at postal code level are predominantly influenced by rate of employment, income, population density, level of education, household size and housing characteristics. In addition, level of congestion attributed to a crash depends on factors including traffic volume, speed, weather, time of the event, severity of the crash, presence of physical barrier separating opposing traffic lanes, work zone, percent of heavy trucks and whether the crash occurred in an urban area or rural area. These results are unequivocal regarding the importance of geographic variation and heterogeneity in driver behavior and the general road safety.en_US
dc.format.extent138 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0002823
dc.identifier.otherZephaniah_alatus_0004D_13360
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3499
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectCivil engineering
dc.subjectTransportation
dc.subjectEconomics
dc.titleA macro-level analysis of safety data using geospatial techniques and spatial econometric methods and modelsen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Civil, Construction, and Environmental Engineering
etdms.degree.disciplineCivil, Construction & Environmental Engineering
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.
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