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Article: Comparative analysis of Bayesian quantile regression models for pedestrian injury severity at signalized intersections

TitleComparative analysis of Bayesian quantile regression models for pedestrian injury severity at signalized intersections
Authors
Keywordspedestrian injury severity
Bayesian binary quantile regression
Bayesian quantile regression for ordinal model
signalized intersection
Issue Date2021
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/utss20
Citation
Journal of Transportation Safety & Security, 2021, v. 13 n. 9, p. 978-999 How to Cite?
AbstractThis study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the heterogeneity issue at signalized intersections. To achieve the objectives, Bayesian binary and ordinal quantile regression models were proposed to address the pedestrian injury severity at signalized intersections. The suitability of the proposed method was illustrated with the Hong Kong dataset from 2008 to 2012 and 376 signalized intersections involving 2090 pedestrian-related crashes are selected. It’s found that age, injury location, pedestrian special circumstance, pedestrian contributory and the presence of Tram/LRT stops and right turning pocket are significant variables. The results indicated that both Bayesian binary and ordinal quantile regression models not only provide a more comprehensive and in-depth understanding of the relationship between pedestrian injury severity and the explanatory variables, but also highlight the heterogeneity issue for the data collected at different locations and different times without many assumptions. The goodness-of-fit of the proposed models outperforms existing mean models, while the Bayesian binary quantile model provides a better fit than the Bayesian quantile regression for ordinal model. The results can benefit the pedestrian facilities improvement/management and guide a much safer pedestrian environment.
Persistent Identifierhttp://hdl.handle.net/10722/304948
ISSN
2023 Impact Factor: 2.4
2023 SCImago Journal Rankings: 0.699
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, X-
dc.contributor.authorLuo, X-
dc.contributor.authorXiao, D-
dc.contributor.authorWong, SC-
dc.date.accessioned2021-10-05T02:37:32Z-
dc.date.available2021-10-05T02:37:32Z-
dc.date.issued2021-
dc.identifier.citationJournal of Transportation Safety & Security, 2021, v. 13 n. 9, p. 978-999-
dc.identifier.issn1943-9962-
dc.identifier.urihttp://hdl.handle.net/10722/304948-
dc.description.abstractThis study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the heterogeneity issue at signalized intersections. To achieve the objectives, Bayesian binary and ordinal quantile regression models were proposed to address the pedestrian injury severity at signalized intersections. The suitability of the proposed method was illustrated with the Hong Kong dataset from 2008 to 2012 and 376 signalized intersections involving 2090 pedestrian-related crashes are selected. It’s found that age, injury location, pedestrian special circumstance, pedestrian contributory and the presence of Tram/LRT stops and right turning pocket are significant variables. The results indicated that both Bayesian binary and ordinal quantile regression models not only provide a more comprehensive and in-depth understanding of the relationship between pedestrian injury severity and the explanatory variables, but also highlight the heterogeneity issue for the data collected at different locations and different times without many assumptions. The goodness-of-fit of the proposed models outperforms existing mean models, while the Bayesian binary quantile model provides a better fit than the Bayesian quantile regression for ordinal model. The results can benefit the pedestrian facilities improvement/management and guide a much safer pedestrian environment.-
dc.languageeng-
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandfonline.com/loi/utss20-
dc.relation.ispartofJournal of Transportation Safety & Security-
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Transportation Safety & Security on 26 Dec 2019, available online: http://www.tandfonline.com/10.1080/19439962.2019.1703867-
dc.subjectpedestrian injury severity-
dc.subjectBayesian binary quantile regression-
dc.subjectBayesian quantile regression for ordinal model-
dc.subjectsignalized intersection-
dc.titleComparative analysis of Bayesian quantile regression models for pedestrian injury severity at signalized intersections-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturepostprint-
dc.identifier.doi10.1080/19439962.2019.1703867-
dc.identifier.scopuseid_2-s2.0-85077354247-
dc.identifier.hkuros325782-
dc.identifier.volume13-
dc.identifier.issue9-
dc.identifier.spage978-
dc.identifier.epage999-
dc.identifier.isiWOS:000504675400001-
dc.publisher.placeUnited States-

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