A comparison of Bayesian and classical statistical techniques used to identify hazardous traffic intersections

Persistent Link:
http://hdl.handle.net/10150/276795
Title:
A comparison of Bayesian and classical statistical techniques used to identify hazardous traffic intersections
Author:
Hecht, Marie B.
Issue Date:
1988
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The accident rate at an intersection is one attribute used to evaluate the hazard associated with the intersection. Two techniques traditionally used to make such evaluations are the rate-quality technique and a technique based on the confidence interval of classical statistics. Both of these techniques label intersections as hazardous if their accident rate is greater than some critical accident rate determined by the technique. An alternative technique is one based on a Bayesian analysis of available accident number and traffic volume data. In contrast to the two classic techniques, the Bayesian technique identifies an intersection as hazardous based on a probabilistic assessment of accident rates. The goal of this thesis is to test and compare the ability of the three techniques to accurately identify traffic intersections known to be hazardous. Test data is generated from an empirical distribution of accident rates. The techniques are then applied to the generated data and compared based on the simulation results.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Roads -- Interchanges and intersections.; Risk assessment -- Statistical methods.; Roads -- Arizona -- Pima County.; Bayesian statistical decision theory.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Systems and Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Higle, Julia

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA comparison of Bayesian and classical statistical techniques used to identify hazardous traffic intersectionsen_US
dc.creatorHecht, Marie B.en_US
dc.contributor.authorHecht, Marie B.en_US
dc.date.issued1988en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe accident rate at an intersection is one attribute used to evaluate the hazard associated with the intersection. Two techniques traditionally used to make such evaluations are the rate-quality technique and a technique based on the confidence interval of classical statistics. Both of these techniques label intersections as hazardous if their accident rate is greater than some critical accident rate determined by the technique. An alternative technique is one based on a Bayesian analysis of available accident number and traffic volume data. In contrast to the two classic techniques, the Bayesian technique identifies an intersection as hazardous based on a probabilistic assessment of accident rates. The goal of this thesis is to test and compare the ability of the three techniques to accurately identify traffic intersections known to be hazardous. Test data is generated from an empirical distribution of accident rates. The techniques are then applied to the generated data and compared based on the simulation results.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectRoads -- Interchanges and intersections.en_US
dc.subjectRisk assessment -- Statistical methods.en_US
dc.subjectRoads -- Arizona -- Pima County.en_US
dc.subjectBayesian statistical decision theory.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHigle, Juliaen_US
dc.identifier.proquest1334351en_US
dc.identifier.oclc21326021en_US
dc.identifier.bibrecord.b17214166en_US
dc.identifier.bibrecord.b17214154en_US
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