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dc.contributor.authorChiou, Yu-Chiunen_US
dc.contributor.authorHwang, Cherng-Chwanen_US
dc.contributor.authorChang, Chih-Chinen_US
dc.contributor.authorFu, Chiangen_US
dc.date.accessioned2014-12-08T15:33:48Z-
dc.date.available2014-12-08T15:33:48Z-
dc.date.issued2013-12-01en_US
dc.identifier.issn0001-4575en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.aap.2013.07.005en_US
dc.identifier.urihttp://hdl.handle.net/11536/23347-
dc.description.abstractThis study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified driver type (age >65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectTwo-vehicle accidentsen_US
dc.subjectBivariate ordered probiten_US
dc.subjectBivariate generalized ordered probiten_US
dc.subjectSeverity levelen_US
dc.title"Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"(Reprinted from Accident Analysis and Prevention, vol.51, pg 175, 2013)en_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.aap.2013.07.005en_US
dc.identifier.journalACCIDENT ANALYSIS AND PREVENTIONen_US
dc.citation.volume61en_US
dc.citation.issueen_US
dc.citation.spage97en_US
dc.citation.epage106en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000328093500012-
dc.citation.woscount0-
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