In practice, crash and injury counts are modeled by using a single equation or a series of independently specified equations, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to biases in sample databases. This paper offers a multivariate Poisson specification that simultaneously models injuries by severity. Parameter estimation is performed within the Bayesian paradigm with a Gibbs sampler for crashes on Washington State highways. Parameter estimates and goodness-of-fit measures are compared with a series of independent Poisson equations, and a cost–benefit analysis of a 10-mph speed limit change is provided as an example application.
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1950 , 1 ; 24-34
2006-01-01
Article (Journal)
Electronic Resource
English
Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
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