Bayesian hierarchical models for linear networks
dc.contributor.author | Al-kaabawi, Z | |
dc.contributor.author | Wei, Yinghui | |
dc.contributor.author | Moyeed, Rana | |
dc.date.accessioned | 2021-01-03T18:07:32Z | |
dc.date.available | 2021-01-03T18:07:32Z | |
dc.date.issued | 2020-12-29 | |
dc.identifier.issn | 0266-4763 | |
dc.identifier.issn | 1360-0532 | |
dc.identifier.uri | http://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.extent | 1-28 | |
dc.format.medium | Electronic-eCollection | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Taylor & Francis (Routledge) | |
dc.subject | Hierarchical models | |
dc.subject | Bayesian methods | |
dc.subject | linear networks | |
dc.subject | point processes | |
dc.title | Bayesian hierarchical models for linear networks | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000603878200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 6 | |
plymouth.volume | 49 | |
plymouth.publication-status | Published | |
plymouth.journal | Journal of Applied Statistics | |
dc.identifier.doi | 10.1080/02664763.2020.1864814 | |
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.place | England | |
dcterms.dateAccepted | 2020-12-12 | |
dc.rights.embargodate | 2022-12-29 | |
dc.identifier.eissn | 1360-0532 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1080/02664763.2020.1864814 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2020-12-29 | |
rioxxterms.type | Journal Article/Review |