Analyzing injury crashes using random-parameter bivariate regression models

作者：Dong Chunjiao^*; Clarke David B; Nambisan Shashi S; Huang Baoshan

来源：Transportmetrica A-Transport Science, 2016, 12(9): 794-810.

DOI：10.1080/23249935.2016.1177134

• Highway safety
• crash frequency
• geometric design features
• random-parameter bivariate regression model
• Bayesian method

This paper proposes a random-parameter bivariate zero-inflated negative binomial (RBZINB) regression model for analyzing the effects of investigated variables on crash frequencies. A Bayesian approach is employed as the estimation method, which has the strength of accounting for the uncertainties related to models and parameter values. The modeling framework has been applied to the bivariate injury crash counts obtained from 1000 intersections in Tennessee over a five-year period. The results reveal that the proposed RBZINB model outperforms other investigated models and provides a superior fit. The proposed RBZINB model is useful in gaining new insights into how crash occurrences are influenced by the risk factors. In addition, the empirical studies show that the proposed RBZINB model has a smaller prediction bias and variance, as well as more accurate coverage probability in estimating model parameters and crash-free probabilities.

• 出版日期2016