@article{ref1,
title="Reprint of "Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"",
journal="Accident analysis and prevention",
year="2013",
author="Chiou, Yu-Chiun and Hwang, Cherng-Chwan and Chang, Chih-Chin and Fu, Chiang",
volume="61",
number="",
pages="97-106",
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.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2013.07.005",
url="http://dx.doi.org/10.1016/j.aap.2013.07.005"
}