On the nature of over-dispersion in motor vehicle crash prediction models

Mitra, Sudeshna and Washington, Simon (2007) On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention, 39 3: 459-468. doi:10.1016/j.aap.2006.08.002

Author Mitra, Sudeshna
Washington, Simon
Title On the nature of over-dispersion in motor vehicle crash prediction models
Journal name Accident Analysis and Prevention   Check publisher's open access policy
ISSN 0001-4575
Publication date 2007-03-01
Year available 2006
Sub-type Article (original research)
DOI 10.1016/j.aap.2006.08.002
Open Access Status Not yet assessed
Volume 39
Issue 3
Start page 459
End page 468
Total pages 10
Place of publication Langford Lane, Oxford, United Kingdom
Publisher Elsevier
Language eng
Abstract Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts-variation over and above that accounted for by the Poisson density. The extra-variation - or dispersion - is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models-tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31-40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites.
Keyword Bayesian method
Crash prediction
Intersection safety
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 114 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 127 times in Scopus Article | Citations
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Created: Wed, 08 Mar 2017, 16:11:16 EST by Jeannette Watson on behalf of Learning and Research Services (UQ Library)