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Title:

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

Accession Number:

01020890

Record Type:

Component

Availability:

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Washington, DC 20001 United States
Order URL: http://www.trb.org/Main/Public/Blurbs/158003.aspx

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Order URL: http://worldcat.org/isbn/0309099595

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.

Monograph Accession #:

01030706

Language:

English

Authors:

Ma, Jianming
Kockelman, Kara M

Pagination:

24p

Publication Date:

2006

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 1950
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

0309099595

Media Type:

Print

Features:

Figures (1) ; References (64) ; Tables (9)

Uncontrolled Terms:

Geographic Terms:

Subject Areas:

Data and Information Technology; Highways; Safety and Human Factors; I81: Accident Statistics

Files:

TRIS, TRB

Created Date:

Mar 3 2006 10:49AM

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