Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity

dc.contributor.author Liu, Chenhui
dc.contributor.author Sharma, Anuj
dc.contributor.author Sharma, Anuj
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Statistics
dc.date 2018-04-02T21:24:19.000
dc.date.accessioned 2020-06-30T01:12:35Z
dc.date.available 2020-06-30T01:12:35Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.embargo 2019-03-01
dc.date.issued 2018-03-01
dc.description.abstract <p>Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatio-temporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatio-temporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major injury and minor injury crashes but not for low-count fatal crashes. High-risk counties were identified using the posterior expected rank by the predicted crash cost rate, which was more able to truly represent the underlying traffic safety status than the rank by the crude crash cost rate.</p>
dc.description.comments <p>This is a manuscript of an article published as Liu, Chenhui, and Anuj Sharma. "Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity." <em>Analytic Methods in Accident Research</em> 17 (2018): 14-31. DOI: <a href="http://dx.doi.org/10.1016/j.amar.2018.02.001" target="_blank">10.1016/j.amar.2018.02.001</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_pubs/179/
dc.identifier.articleid 1183
dc.identifier.contextkey 11889450
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/179
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13824
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_pubs/179/2018_Sharma_UsingMultivariate.pdf|||Fri Jan 14 21:30:42 UTC 2022
dc.source.uri 10.1016/j.amar.2018.02.001
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Multivariate Analysis
dc.subject.disciplines Transportation Engineering
dc.subject.keywords Multivariate spatio-temporal
dc.subject.keywords Bayesian
dc.subject.keywords Crash frequency
dc.subject.keywords Posterior expected rank
dc.subject.keywords Crash cost rate
dc.title Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 717eae32-77e8-420a-b66c-a44c60495a6b
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
File
Original bundle
Now showing 1 - 1 of 1
Name:
2018_Sharma_UsingMultivariate.pdf
Size:
1.44 MB
Format:
Adobe Portable Document Format
Description:
Collections