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First published January 2006

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

Jianming Ma and Kara M. KockelmanView all authors and affiliations

Volume 1950, Issue 1

https://doi.org/10.1177/0361198106195000104

Abstract

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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Transportation Research Record

Volume 1950, Issue 1

Pages: 24 - 34

Article first published: January 2006

Issue published: January 2006

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Authors

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Jianming Ma

University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076.

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Kara M. Kockelman

University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076.

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