Identification of hazardous motor vehicle accident sites: some Bayesian considerations

Appropriate hazardous accident site identification and discrimination is a fundamental difficulty that confronts traffic safety researchers. Readily employed Bayesian methods can redress this difficulty and are the focus of this thesis. Accident analysis, including hazardous site identification, in...

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Main Author: Schlüter, Philip John
Language:en
Published: University of Canterbury. Mathematics 2013
Online Access:http://hdl.handle.net/10092/8428
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spelling ndltd-canterbury.ac.nz-oai-ir.canterbury.ac.nz-10092-84282015-03-30T15:29:42ZIdentification of hazardous motor vehicle accident sites: some Bayesian considerationsSchlüter, Philip JohnAppropriate hazardous accident site identification and discrimination is a fundamental difficulty that confronts traffic safety researchers. Readily employed Bayesian methods can redress this difficulty and are the focus of this thesis. Accident analysis, including hazardous site identification, invariably requires the specification of some defined distributional function. However, several different distributions have been proposed to model traffic accidents, and so the most suitable model amongst these must be appropriately determined and selected. Model selection should satisfactorily fulfill two requisite criteria; firstly, that the best model is discriminated, and secondly, that this best distribution adequately describes the data. To help satisfy these requirements we introduce the averaged Bayes factor, a new method that determines the best model from likely candidate distributions, and we propose a new Bayesian procedure that facilitates the quantitative assessment of model adequacy. In addition, a method quantifying the power of detecting model inadequacy is presented. With the specification of an appropriate accident distribution, procedures facilitating hazardous site identification, ranking and selection are then proposed. These procedures are accomplished using the hierarchical Bayesian method and three intuitive quantitative strategies. Especially useful is a variation of the posterior probability that gives the probability each particular site is worst and by how much it is worst. All proposed techniques are illustrated using previously published accident data from 35 sites in Auckland, New Zealand.University of Canterbury. Mathematics2013-10-07T04:25:06Z2013-10-07T04:25:06Z1996Electronic thesis or dissertationTexthttp://hdl.handle.net/10092/8428enNZCUCopyright Philip John Schlüterhttp://library.canterbury.ac.nz/thesis/etheses_copyright.shtml
collection NDLTD
language en
sources NDLTD
description Appropriate hazardous accident site identification and discrimination is a fundamental difficulty that confronts traffic safety researchers. Readily employed Bayesian methods can redress this difficulty and are the focus of this thesis. Accident analysis, including hazardous site identification, invariably requires the specification of some defined distributional function. However, several different distributions have been proposed to model traffic accidents, and so the most suitable model amongst these must be appropriately determined and selected. Model selection should satisfactorily fulfill two requisite criteria; firstly, that the best model is discriminated, and secondly, that this best distribution adequately describes the data. To help satisfy these requirements we introduce the averaged Bayes factor, a new method that determines the best model from likely candidate distributions, and we propose a new Bayesian procedure that facilitates the quantitative assessment of model adequacy. In addition, a method quantifying the power of detecting model inadequacy is presented. With the specification of an appropriate accident distribution, procedures facilitating hazardous site identification, ranking and selection are then proposed. These procedures are accomplished using the hierarchical Bayesian method and three intuitive quantitative strategies. Especially useful is a variation of the posterior probability that gives the probability each particular site is worst and by how much it is worst. All proposed techniques are illustrated using previously published accident data from 35 sites in Auckland, New Zealand.
author Schlüter, Philip John
spellingShingle Schlüter, Philip John
Identification of hazardous motor vehicle accident sites: some Bayesian considerations
author_facet Schlüter, Philip John
author_sort Schlüter, Philip John
title Identification of hazardous motor vehicle accident sites: some Bayesian considerations
title_short Identification of hazardous motor vehicle accident sites: some Bayesian considerations
title_full Identification of hazardous motor vehicle accident sites: some Bayesian considerations
title_fullStr Identification of hazardous motor vehicle accident sites: some Bayesian considerations
title_full_unstemmed Identification of hazardous motor vehicle accident sites: some Bayesian considerations
title_sort identification of hazardous motor vehicle accident sites: some bayesian considerations
publisher University of Canterbury. Mathematics
publishDate 2013
url http://hdl.handle.net/10092/8428
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