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Article: Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression
Title | Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression |
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Authors | |
Keywords | Areal traffic safety crash rates by severity spatio-temporal correlation unobserved heterogeneity multivariate random-parameters Tobit model |
Issue Date | 2019 |
Publisher | Taylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21 |
Citation | Transportmetrica A: Transport Science, 2019, v. 15 n. 2, p. 1867-1884 How to Cite? |
Abstract | This study investigates the inclusion of spatio-temporal correlation and interaction in a multivariate random-parameters Tobit model and their influence on fitting areal crash rates with different severity outcomes. The spatial correlation is specified via a multivariate conditional autoregressiv (MCAR) prior, whereas the temporal correlation is specified by a linear time trend. A spatio-temporal interaction is formulated as the product of a time trend and a spatial term with an MCAR prior. A multivariate random-parameters spatio-temporal Tobit model is developed for slight injury and killed or serious injury crash rates using one year of crash data from 131 traffic analysis zones in Hong Kong. The proposed model is estimated and assessed in the Bayesian context. The model estimation results show that spatial and temporal effects and their interactive effects are significant and that the spatial and interactive effects have strong correlations across injury severities. The proposed model outperforms a multivariate random-parameters Tobit model and a multivariate random-parameters spatial Tobit model in terms of model fit. These findings highlight the importance of appropriately accommodating spatio-temporal correlation and interaction for the joint analysis of areal crash rates by severity. |
Persistent Identifier | http://hdl.handle.net/10722/280950 |
ISSN | 2023 Impact Factor: 3.6 2023 SCImago Journal Rankings: 1.099 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zeng, Q | - |
dc.contributor.author | Guo, Q | - |
dc.contributor.author | Wong, SC | - |
dc.contributor.author | Wen, H | - |
dc.contributor.author | Huang, H | - |
dc.contributor.author | Pei, X | - |
dc.date.accessioned | 2020-02-25T07:43:10Z | - |
dc.date.available | 2020-02-25T07:43:10Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Transportmetrica A: Transport Science, 2019, v. 15 n. 2, p. 1867-1884 | - |
dc.identifier.issn | 2324-9935 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280950 | - |
dc.description.abstract | This study investigates the inclusion of spatio-temporal correlation and interaction in a multivariate random-parameters Tobit model and their influence on fitting areal crash rates with different severity outcomes. The spatial correlation is specified via a multivariate conditional autoregressiv (MCAR) prior, whereas the temporal correlation is specified by a linear time trend. A spatio-temporal interaction is formulated as the product of a time trend and a spatial term with an MCAR prior. A multivariate random-parameters spatio-temporal Tobit model is developed for slight injury and killed or serious injury crash rates using one year of crash data from 131 traffic analysis zones in Hong Kong. The proposed model is estimated and assessed in the Bayesian context. The model estimation results show that spatial and temporal effects and their interactive effects are significant and that the spatial and interactive effects have strong correlations across injury severities. The proposed model outperforms a multivariate random-parameters Tobit model and a multivariate random-parameters spatial Tobit model in terms of model fit. These findings highlight the importance of appropriately accommodating spatio-temporal correlation and interaction for the joint analysis of areal crash rates by severity. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21 | - |
dc.relation.ispartof | Transportmetrica A: Transport Science | - |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica A: Transport Science on 21 Aug 2019 available online: http://www.tandfonline.com/10.1080/23249935.2019.1652867 | - |
dc.subject | Areal traffic safety | - |
dc.subject | crash rates by severity | - |
dc.subject | spatio-temporal correlation | - |
dc.subject | unobserved heterogeneity | - |
dc.subject | multivariate random-parameters Tobit model | - |
dc.title | Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression | - |
dc.type | Article | - |
dc.identifier.email | Wong, SC: hhecwsc@hku.hk | - |
dc.identifier.authority | Wong, SC=rp00191 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1080/23249935.2019.1652867 | - |
dc.identifier.scopus | eid_2-s2.0-85071163205 | - |
dc.identifier.hkuros | 309189 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1867 | - |
dc.identifier.epage | 1884 | - |
dc.identifier.isi | WOS:000482209400001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2324-9935 | - |