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

Product Type

Journal Article

Author

Ma, J. and K.M. Kockelman

Date

Jan-06

Full citation

Ma, J. and K.M. Kockelman. Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity. In Transportation Research Record: Journal of the Transportation Research Board, No. 1950, Transportation Research Board, National Research Council, Washington, D.C., 2006, pp. 24-34

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 costbenefit analysis of a 10-mph speed limit change is provided as an example application.

Available From

Transportation Research Board

Link To Journal Article

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

Keywords

Bayesian approach
Benefit cost analysis
Crash injuries
Injury severity
Multivariate analysis
Poisson ratio
Regression analysis
Speed limits


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