Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach

Yu-Chiun Chiou*, Cherng Chwan Hwang, Chih Chin Chang, Chiang Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.

Original languageEnglish
Pages (from-to)175-184
Number of pages10
JournalAccident Analysis and Prevention
StatePublished - 1 Jan 2013


  • Bivariate generalized ordered probit
  • Bivariate ordered probit
  • Severity level
  • Two-vehicle accidents

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