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dc.contributor.authorAl-kaabawi, Z
dc.contributor.authorWei, Yinghui
dc.contributor.authorMoyeed, Rana
dc.date.accessioned2021-01-03T18:07:32Z
dc.date.available2021-01-03T18:07:32Z
dc.date.issued2020-12-29
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.urihttp://hdl.handle.net/10026.1/16774
dc.description.abstract

The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.

dc.format.extent1-28
dc.format.mediumElectronic-eCollection
dc.languageen
dc.language.isoen
dc.publisherTaylor & Francis (Routledge)
dc.subjectHierarchical models
dc.subjectBayesian methods
dc.subjectlinear networks
dc.subjectpoint processes
dc.titleBayesian hierarchical models for linear networks
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000603878200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume49
plymouth.publication-statusPublished
plymouth.journalJournal of Applied Statistics
dc.identifier.doi10.1080/02664763.2020.1864814
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences/UoA 10 - Former and non-independent
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeEngland
dcterms.dateAccepted2020-12-12
dc.rights.embargodate2022-12-29
dc.identifier.eissn1360-0532
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1080/02664763.2020.1864814
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-12-29
rioxxterms.typeJournal Article/Review


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