Bayesian hierarchical analysis on crash prediction models

Abstract

In this study, an innovative Bayesian hierarchical method is developed to analyze the traffic crash frequency and severity. A zero-inflated Poisson model with location-specific random effects is proposed to capture both the multilevel data structure and excess zeros in crash frequency prediction. And for crash severity prediction, a hierarchical binomial logistic model is developed to examine the individual severity in the presence of within-crash correlation. Bayesian inference using Markov Chain Monte Carlo algorithm is developed to calibrate the proposed models and a number of Bayesian measures such as the deviance information criterion, cross-validation predictive densities, and intra-class correlation coefficients are employed to establish the model suitability. The proposed method is illustrated using the Singapore crash records. Comparing the predictive abilities of the proposed models against those of traditional methods, the study proved the importance of accounting for the within-cluster correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.