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

Yannis, G; Antoniou, C; Papadimitriou, E

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	https://dspace.lib.ntua.gr/xmlui/handle/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