Fast Bayesian inference for modeling multivariate crash counts

Publication date: March 2016 Source:Analytic Methods in Accident Research, Volume 9 Author(s): Volodymyr Serhiyenko, Sha A. Mamun, John N. Ivan, Nalini Ravishanker This paper investigates the multivariate Poisson Lognormal modeling of counts for different types of crashes. This multivariate model can account for the overdispersion as well as positive and/or negative association between counts. Approximate Bayesian inference via the Integrated Nested Laplace Approximations significantly decreases computational time which makes it attractive for researchers. The models are developed for single vehicle, same direction and opposite direction crash types using three years (2009–2011) of crash data on Connecticut divided limited access highway segments. Annual average daily traffic, segment length, and road specific covariates (median type, shoulder width, area type, and on-ramp indicator) are used as predictor variables. The results from the multivariate Poisson Lognormal model suggest that an increase in the annual average daily traffic, segment length, and shoulder width together with urban area type and presence of an on-ramp are associated with in an increase in crashes. The median type covariate has a mixed effect for different median types on different type of crashes. The multivariate Poisson Lognormal model results are compared with the results obtained from two univariate regression models, univariate Poisson Lognormal and univariate negative binomial, with ...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research