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.


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    Title :

    Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2006-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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