Citation

Chiou YC, Hwang CC, Chang CC, Fu C. Accid. Anal. Prev. 2013; 61: 97-106.

Affiliation

Institute of Traffic and Transportation, National Chiao Tung University, 4F, 118, Sec. 1, Chung-Hsiao W. Rd., Taipei 100, Taiwan. Electronic address: ycchiou@mail.nctu.edu.tw.

Copyright

(Copyright © 2013, Elsevier Publishing)

DOI

10.1016/j.aap.2013.07.005

PMID

23915470

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.

Language: en