TY - JOUR
T1 - Reprint of "modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"
AU - Chiou, Yu Chiun
AU - Hwang, Cherng Chwan
AU - Chang, Chih Chin
AU - Fu, Chiang
N1 - Funding Information:
The authors are indebted to two anonymous reviewers for their insightful comments and constructive suggestions, which help clarify several points made in the original manuscript. This manuscript is revised from an early work presented at the 3rd International Conference on Road Safety and Simulation . The study was partially granted by National Science Council, Republic of China ( NSC 97-2628-E-009-035-MY3 , NSC 100-2221-E-009-12197 , and NSC 101-2628-E-009-018-MY3 ).
PY - 2013
Y1 - 2013
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.
AB - 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.
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U2 - 10.1016/j.aap.2013.07.005
DO - 10.1016/j.aap.2013.07.005
M3 - Article
AN - SCOPUS:84888293574
SN - 0001-4575
VL - 61
SP - 97
EP - 106
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
ER -
