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dc.contributor.authorKennedy, Jason Forresten_US
dc.date.accessioned2014-03-14T20:50:26Z
dc.date.available2014-03-14T20:50:26Z
dc.date.issued2008-12-10en_US
dc.identifier.otheretd-12192008-163243en_US
dc.identifier.urihttp://hdl.handle.net/10919/36308
dc.description.abstractCrash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartThesis_Kennedy_Jan72009.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectCrash modelingen_US
dc.subjectExposure to risken_US
dc.subjectSignalized intersectionsen_US
dc.subjectCrash risk estimationen_US
dc.subjectPedestrian crashesen_US
dc.titleEstimating Pedestrian Crashes at Urban Signalized Intersectionsen_US
dc.typeThesisen_US
dc.contributor.departmentCivil Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineCivil Engineeringen_US
dc.contributor.committeechairRakha, Hesham Ahmeden_US
dc.contributor.committeememberHancock, Kathleen L.en_US
dc.contributor.committeememberMurray-Tuite, Pamela M.en_US
dc.contributor.committeememberKikuchi, Shinyaen_US
dc.contributor.committeememberInge, Patches Johnsonen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12192008-163243/en_US
dc.date.sdate2008-12-19en_US
dc.date.rdate2009-01-07
dc.date.adate2009-01-07en_US


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