TY  - JOUR
PY  - 2013//
TI  - Reprint of "Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"
JO  - Accident analysis and prevention
A1  - Chiou, Yu-Chiun
A1  - Hwang, Cherng-Chwan
A1  - Chang, Chih-Chin
A1  - Fu, Chiang
SP  - 97
EP  - 106
VL  - 61
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0001-4575
UR  - http://dx.doi.org/10.1016/j.aap.2013.07.005
ID  - ref1
ER  -