Improvements in estimating a fatal accidents model formed by an Artificial Neural Network

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dc.contributor.authorCansiz, Omer Faruk
dc.date.accessioned2024-09-18T20:02:40Z
dc.date.available2024-09-18T20:02:40Z
dc.date.issued2011
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThe Smeed Equation (SE) is the first model being used to improve the estimation of the number of dead in accidents that consist of the independent variables of population and number of vehicles and the dependent variable of the number of dead. In this study, the population variable in the SE is replaced with the number of drivers. At first, the SE is made suitable for USA data and the Revised SE is obtained. Then the coefficients are calculated again by the replacement of the number of drivers with population and the Improved SE is obtained. Afterwards, Artificial Neural Network (ANN) models are formed in both variable groups of population and number of drivers. The best ANN model, whose inputs are the number of vehicles and drivers, has 19 neurons, a tan-sig transfer function and a Levenberg-Marquardt training algorithm. In the comparison of ANN models and SE models, the value of R(2) increases from 0.8906 to 0.9695 and the value of mean square errors (MSEs) decreases from 87,503 to 39,310. As a result the replacement of the number of drivers variable with population has a contribution in the estimation of the number of dead in vehicle accidents. This study showed that use of the number of drivers instead of the population in the number of dead prediction can be improved with the accuracy of the proposed models. Moreover, ANN models can be used to predict the number of dead in traffic accidents with a high correlation coefficient and a low MSE according to the SE and loglinear regression methods.en_US
dc.identifier.doi10.1177/0037549710370842
dc.identifier.endpage522en_US
dc.identifier.issn0037-5497
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-79957504352en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage512en_US
dc.identifier.urihttps://doi.org/10.1177/0037549710370842
dc.identifier.urihttps://hdl.handle.net/20.500.12483/7954
dc.identifier.volume87en_US
dc.identifier.wosWOS:000290711000003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofSimulation-Transactions of The Society For Modeling and Simulation Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectfatality in traffic accidentsen_US
dc.subjectmotor-vehicle accidenten_US
dc.subjectnumber of driversen_US
dc.titleImprovements in estimating a fatal accidents model formed by an Artificial Neural Networken_US
dc.typeArticleen_US

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