Use of artificial neural network to estimate number of persons fatally injured in motor vehicle accidents

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dc.authorscopusid8391004900
dc.authorscopusid36619566700
dc.authorscopusid36620032800
dc.contributor.authorCansiz, Omer F.
dc.contributor.authorCalisici, Mustafa
dc.contributor.authorMiroglu, M. Melik
dc.date.accessioned2024-09-19T15:41:15Z
dc.date.available2024-09-19T15:41:15Z
dc.date.issued2009
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description3rd International Conference on Applied Mathematics, Simulation, Modelling, ASM'09, 3rd International Conference on Circuits, Systems and Signals, CSS'09 -- 29 December 2009 through 31 December 2009 -- Athens -- 82304en_US
dc.description.abstractThe paper demonstrates an artificial intelligence method known as the Artificial Neural Network (ANN) approach based on supervised neural networks to estimate the number of persons fatally injured in motor vehicle accidents. In order to analyze a data set related to fatal accidents, the Artificial Neural Network Estimating Fatal Accident (ANNEFA) model is developed by using social and traffic-related variables, population and motorvehicle registrations. To obtain the best form of ANNEFA, various ANN models having different transfer functions, different number of neurons and different train algorithms are designed. The ANNEFA model formed with fourteen neurons, tansig transfer function and Levenberg-Marquardt training algorithm provides the best fit to training and test data. Fluctuations in variables used in historical data are reflected in results of the ANNEFA model. Estimates of the ANNEFA model compared with the results of Revised Smeed Equation (RSE) reconstituted from Smeed Equation in accord with the USA Data Set. Thus, this study provides a benchmark for predicting fatality in motor-vehicle accidents in the form of a numerical and graphical comparison between results of the ANNEFA and RSE. The results indicate that the ANN model is a proper approach in predicting fatalities in motor-vehicle crashes.en_US
dc.identifier.endpage142en_US
dc.identifier.isbn978-960474147-2
dc.identifier.scopus2-s2.0-78149374515en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage136en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14121
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, ASM'09, Proceedings of the 3rd International Conference on Circuits, Systems and Signals, CSS'09en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - 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.titleUse of artificial neural network to estimate number of persons fatally injured in motor vehicle accidentsen_US
dc.typeConference Objecten_US

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