Using artificial neural network to predict collisions on horizontal tangents of 3D two-lane highways

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dc.authorscopusid8391004900
dc.authorscopusid7005999634
dc.contributor.authorCansiz, Omer F.
dc.contributor.authorEasa, Said M.
dc.date.accessioned2024-09-19T15:41:20Z
dc.date.available2024-09-19T15:41:20Z
dc.date.issued2011
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThe purpose of this study is mainly to predict collision frequency on the horizontal tangents combined with vertical curves using artificial neural network methods. The proposed ANN models are compared with existing regression models. First, the variables that affect collision frequency were investigated. It was found that only the annual average daily traffic, section length, access density, the rate of vertical curvature, smaller curve radius before and after the tangent were statistically significant according to related combinations. Second, three statistical models (negative binomial, zero inflated Poisson and zero inflated negative binomial) were developed using the significant variables for three alignment combinations. Third, ANN models are developed by applying the same variables for each combination. The results clearly show that the ANN models have the lowest mean square error value than those of the statistical models. Similarly, the AIC values of the ANN models are smaller to those of the regression models for all the combinations. Consequently, the ANN models have better statistical performances than statistical models for estimating collision frequency. The ANN models presented in this paper are recommended for evaluating the safety impacts 3D alignment elements on horizontal tangents.en_US
dc.identifier.endpage47en_US
dc.identifier.issn2010-3778
dc.identifier.scopus2-s2.0-84857175299en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage38en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14193
dc.identifier.volume82en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofWorld Academy of Science, Engineering and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject3D two-lane highwayen_US
dc.subjectArtificial neural networken_US
dc.subjectCollision frequencyen_US
dc.subjectHorizontal tangenten_US
dc.subjectNegative binomialen_US
dc.subjectZero inflated poissonen_US
dc.titleUsing artificial neural network to predict collisions on horizontal tangents of 3D two-lane highwaysen_US
dc.typeArticleen_US

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