An artificial intelligent approach to traffic accident estimation: Model development and application

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dc.contributor.authorAkgüngör, Ali Payidar
dc.contributor.authorDoğan, Erdem
dc.date.accessioned2020-06-25T17:48:59Z
dc.date.available2020-06-25T17:48:59Z
dc.date.issued2009
dc.departmentKırıkkale Üniversitesi
dc.descriptionAKGUNGOR, ALI PAYIDAR/0000-0003-0669-5715; DOGAN, Erdem/0000-0001-7802-641X
dc.description.abstractThis study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half-fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.en_US
dc.identifier.citationAkgüngör, A. P., & Doğan, E. (2009). An artificial intelligent approach to traffic accident estimation: Model development and application. Transport, 24(2), 135-142. https://doi.org/10.3846/1648-4142.2009.24.135-142en_US
dc.identifier.doi10.3846/1648-4142.2009.24.135-142
dc.identifier.endpage142en_US
dc.identifier.issn1648-4142
dc.identifier.issn1648-3480
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-70349556668
dc.identifier.scopusqualityQ2
dc.identifier.startpage135en_US
dc.identifier.urihttps://doi.org/10.3846/1648-4142.2009.24.135-142
dc.identifier.urihttps://hdl.handle.net/20.500.12587/4609
dc.identifier.volume24en_US
dc.identifier.wosWOS:000267724800007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherVilnius Gediminas Tech Univen_US
dc.relation.ispartofTransport
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectaccident prediction modelen_US
dc.subjectartificial neural network (ANN)en_US
dc.subjectgenetic algorithmen_US
dc.subjectinjuryen_US
dc.subjectAnkaraen_US
dc.titleAn artificial intelligent approach to traffic accident estimation: Model development and applicationen_US
dc.typeArticle

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