Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

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dc.contributor.authorDoğan, Erdem
dc.contributor.authorAkgüngör, Ali Payidar
dc.date.accessioned2020-06-25T18:07:23Z
dc.date.available2020-06-25T18:07:23Z
dc.date.issued2013
dc.departmentKırıkkale Üniversitesi
dc.descriptionAKGUNGOR, ALI PAYIDAR/0000-0003-0669-5715; DOGAN, Erdem/0000-0001-7802-641X
dc.description.abstractThis study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980-2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R (2)). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1007/s00521-011-0778-0
dc.identifier.endpage877en_US
dc.identifier.issn0941-0643
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-84875062122
dc.identifier.scopusqualityQ1
dc.identifier.startpage869en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-011-0778-0
dc.identifier.urihttps://hdl.handle.net/20.500.12587/5559
dc.identifier.volume22en_US
dc.identifier.wosWOS:000316394000004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHighway accidentsen_US
dc.subjectArtificial neural networksen_US
dc.subjectNonlinear modelingen_US
dc.subjectTraffic safetyen_US
dc.subjectTurkeyen_US
dc.titleForecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networksen_US
dc.typeArticle

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