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dc.contributor.authorMcCarthy, Ross Jamesen_US
dc.date.accessioned2015-09-18T20:03:44Z
dc.date.available2015-09-18T20:03:44Z
dc.date.issued2015-09-09en_US
dc.identifier.othervt_gsexam:6097en_US
dc.identifier.urihttp://hdl.handle.net/10919/56576
dc.description.abstractEvaluation of crash count data as a function of roadway characteristics allows Departments of Transportation to predict expected average crash risks in order to assist in identifying segments that could benefit from various treatments. Currently, the evaluation is performed using negative binomial regression, as a function of average annual daily traffic (AADT) and other variables. For this thesis, a crash study was carried out for the interstate, primary and secondary routes, in the Salem District of Virginia. The data used in the study included the following information obtained from Virginia Department of Transportation (VDOT) records: 2010 to 2012 crash data, 2010 to 2012 AADT, and horizontal radius of curvature (CV). Additionally, tire-pavement friction or skid resistance was measured using a continuous friction measurement, fixed-slip device called a Grip Tester. In keeping with the current practice, negative binomial regression was used to relate the crash data to the AADT, skid resistance and CV. To determine which of the variables to include in the final models, the Akaike Information Criterion (AIC) and Log-Likelihood Ratio Tests were performed. By mathematically combining the information acquired from the negative binomial regression models and the information contained in the crash counts, the parameters of each network's true average crash risks were empirically estimated using the Empirical Bayes (EB) approach. The new estimated average crash risks were then used to rank segments according to their empirically estimated crash risk and to prioritize segments according to their expected crash reduction if a friction treatment were applied.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectskid resistanceen_US
dc.subjectPoissonen_US
dc.subjectPoisson-Gammaen_US
dc.subjectNegative Binomialen_US
dc.subjectSafety Performance Functionen_US
dc.subjectEmpirical Bayesen_US
dc.titlePerforming Network Level Crash Evaluation Using Skid Resistanceen_US
dc.typeThesisen_US
dc.contributor.departmentCivil and Environmental Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineCivil Engineeringen_US
dc.contributor.committeechairFlintsch, Gerardo W.en_US
dc.contributor.committeememberMcGhee, Kevin Kennethen_US
dc.contributor.committeememberde Leon Izeppi, Edgar Den_US
dc.contributor.committeememberParry, Tonyen_US


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