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Road casualties and enforcement: Distributional assumptions of serially correlated count data

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dc.contributor.author Yannis, G en
dc.contributor.author Antoniou, C en
dc.contributor.author Papadimitriou, E en
dc.date.accessioned 2014-03-01T01:27:05Z
dc.date.available 2014-03-01T01:27:05Z
dc.date.issued 2007 en
dc.identifier.issn 15389588 en
dc.identifier.uri http://hdl.handle.net/123456789/18330
dc.subject Enforcement en
dc.subject Generalized Linear Models en
dc.subject Negative Binomial en
dc.subject Poisson en
dc.subject Road Safety en
dc.subject Serial Correlation en
dc.subject.other article en
dc.subject.other biological model en
dc.subject.other car driving en
dc.subject.other Greece en
dc.subject.other human en
dc.subject.other law enforcement en
dc.subject.other mortality en
dc.subject.other statistical model en
dc.subject.other traffic accident en
dc.subject.other Accidents, Traffic en
dc.subject.other Automobile Driving en
dc.subject.other Greece en
dc.subject.other Humans en
dc.subject.other Law Enforcement en
dc.subject.other Linear Models en
dc.subject.other Models, Biological en
dc.title Road casualties and enforcement: Distributional assumptions of serially correlated count data en
heal.type journalArticle en
heal.identifier.primary 10.1080/15389580701369241 en
heal.identifier.secondary http://dx.doi.org/10.1080/15389580701369241 en
heal.publicationDate 2007 en
heal.abstract Objective. Road safety data are often in the form of counts and usually temporally correlated. The objective of this research is to investigate the distributional assumptions of road safety data in the presence of temporal correlation. Methods. Using the generalized linear model framework, four distributional assumptions are considered: normal, Poisson, quasi-Poisson and negative binomial, and appropriate models are estimated. Monthly casualty and police enforcement data from Greece for a period of six years (January 1998-December 2003) have been used. The developed models include sinusoidal latent terms to capture the temporal serial correlation of observations. Several statistical goodness-of-fit diagnostic tests have been performed for the results of the estimated models, and the predictive capabilities of the models are investigated. Results. The residuals of the quasi-Poisson and negative binomial models do not show any serial correlation. The signs of the estimated coefficients for all models are consistent and intuitive. In particular, a negative coefficient value for the number of breath alcohol controls indicates that the number of persons killed and seriously injured decreases as the intensity of breath alcohol controls increases. The Poisson model fails to capture the overdispersion in the data, thus underestimating the standard errors of the estimated coefficients. Conclusion. The results suggest that the quasi-Poisson and negative binomial outperform the normal and Poisson models in this application. The findings of this research demonstrate a clear link between the intensification of police enforcement and the reduction of traffic accident casualties. In particular, an increase in the number of breath alcohol controls in Greece after 1998 contributed to a reduction in the number of persons killed and seriously injured from traffic accidents. Copyright © 2007 Taylor & Francis Group, LLC. en
heal.journalName Traffic Injury Prevention en
dc.identifier.doi 10.1080/15389580701369241 en
dc.identifier.volume 8 en
dc.identifier.issue 3 en
dc.identifier.spage 300 en
dc.identifier.epage 308 en


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