Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5943
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dc.contributor.authorKoşun, Çağlar-
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorÇelik, Hüseyin Murat-
dc.date.accessioned2017-07-18T07:23:30Z
dc.date.available2017-07-18T07:23:30Z
dc.date.issued2016
dc.identifier.citationKoşun, Ç., Tayfur, G., and Çelik, H. M. (2016). Soft computing and regression modelling approaches for link-capacity functions. Neural Network World, 26(2), 129-140. doi:10.14311/NNW.2016.26.007en_US
dc.identifier.issn1210-0552
dc.identifier.issn2336-4335-
dc.identifier.issn1210-0552-
dc.identifier.urihttp://doi.org/10.14311/NNW.2016.26.007
dc.identifier.urihttp://hdl.handle.net/11147/5943
dc.description.abstractLink-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.en_US
dc.language.isoenen_US
dc.publisherCzech Technical University in Pragueen_US
dc.relation.ispartofNeural Network Worlden_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectFlow rateen_US
dc.subjectLink capacitiesen_US
dc.subjectRegression analysisen_US
dc.subjectTravel timeen_US
dc.subjectTraffic controlen_US
dc.titleSoft computing and regression modelling approaches for link-capacity functionsen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorKoşun, Çağlar-
dc.institutionauthorTayfur, Gökmen-
dc.departmentIzmir Institute of Technology. City and Regional Planningen_US
dc.identifier.volume26en_US
dc.identifier.issue2en_US
dc.identifier.startpage129en_US
dc.identifier.endpage140en_US
dc.identifier.wosWOS:000376334400002
dc.identifier.scopusSCOPUS:2-s2.0-84987728121
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.14311/NNW.2016.26.007-
dc.relation.doi10.14311/NNW.2016.26.007en_US
dc.coverage.doi10.14311/NNW.2016.26.007en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.author.deptDepartment of Civil Engineering-
Appears in Collections:City and Regional Planning / Şehir ve Bölge Planlama
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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