Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach |
| |
Authors: | Yu-Chiun Chiou Cherng-Chwan HwangChih-Chin Chang Chiang Fu |
| |
Affiliation: | Institute of Traffic and Transportation, National Chiao Tung University, 4F, 118, Sec. 1, Chung-Hsiao W. Rd., Taipei 100, Taiwan |
| |
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. |
| |
Keywords: | Two-vehicle accidents Bivariate ordered probit Bivariate generalized ordered probit Severity level |
本文献已被 ScienceDirect 等数据库收录! |
|