Analyzing crash frequency and severity data using novel techniques

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dc.contributorLindly, Jay K.
dc.contributorAnderson, Michael David
dc.contributorDurrans, S. Rocky
dc.contributor.advisorJones, Steven L.
dc.contributor.advisorLou, Yingyan
dc.contributor.authorMehta, Gaurav Satish
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-03-01T17:12:26Z
dc.date.available2017-03-01T17:12:26Z
dc.date.issued2014
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractProviding safe travel from one point to another is the main objective of any public transportation agency. The recent publication of the Highway Safety Manual (HSM) has resulted in an increasing emphasis on the safety performance of specific roadway facilities. The HSM provides tools such as crash prediction models that can be used to make informed decisions. The manual is a good starting point for transportation agencies interested in improving roadway safety in their states. However, the models published in the manual need calibration to account for the local driver behavior and jurisdictional changes. The method provided in the HSM for calibrating crash prediction models is not scientific and has been proved inefficient by several studies. To overcome this limitation this study proposes two alternatives. Firstly, a new method is proposed for calibrating the crash prediction models using negative binomial regression. Secondly, this study investigates new forms of state-specific Safety Performance Function SPFs using negative binomial techniques. The HSM's 1st edition provides a multiplier applied to the univariate crash prediction models to estimate the expected number of crashes for different crash severities. It does not consider the distinct effect unobserved heterogeneity might have on crash severities. To address this limitation, this study developed a multivariate extension of the Conway Maxwell Poisson distribution for predicting crashes. This study gives the statistical properties and the parameter estimation algorithm for the distribution. The last part of this dissertation extends the use of Highway Safety Manual by developing a multivariate crash prediction model for the bridge section of the roads. The study then compares the performance of the newly proposed multivariate Conway Maxwell Poisson (MVCMP) model with the multivariate Poisson Lognormal, univariate Conway Maxwell Poisson (UCMP) and univariate Poisson Lognormal model for different crash severities. This example will help transportation researchers in applying the model correctly.en_US
dc.format.extent158 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0001738
dc.identifier.otherMehta_alatus_0004D_12186
dc.identifier.urihttps://ir.ua.edu/handle/123456789/2187
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 planning
dc.titleAnalyzing crash frequency and severity data using novel techniquesen_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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