title: Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach.
abstract: This 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.
authors.affiliation: Institute of Traffic and Transportation, National Chiao Tung University, 4F, 118, Sec. 1, Chung-Hsiao W. Rd., Taipei 100, Taiwan. ycchiou@mail.nctu.edu.tw
authors.forename: Yu-Chiun
authors.initials: YC
authors.lastname: Chiou
authors.forename: Cherng-Chwan
authors.initials: CC
authors.lastname: Hwang
authors.forename: Chih-Chin
authors.initials: CC
authors.lastname: Chang
authors.forename: Chiang
authors.initials: C
authors.lastname: Fu
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D002648:Child; D002675:Child, Preschool; D002947:Cities; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012307:Risk Factors; D013624:Taiwan; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000209:etiology* / Q000401:mortality; D055815:Young Adult