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dc.contributor.authorHatamzad, Mahshid
dc.contributor.authorPolanco Pinerez, Geanette Cleotilde
dc.contributor.authorCasselgren, Johan
dc.date.accessioned2022-11-22T10:12:33Z
dc.date.available2022-11-22T10:12:33Z
dc.date.issued2022-04-06
dc.description.abstractSince Winter Road Maintenance (WRM) is an important activity in Nordic countries, accurate intelligent cost-effective WRM can create precise advance plans for developing decision support systems to improve traffic safety on the roads, while reducing cost and negative environmental impacts. Lack of comprehensive knowledge and inaccurate WRM information would lead to a certain loss of WRM budget, safety reduction, and irreparable environmental damage. This study proposes an intelligent methodology that uses data envelopment analysis and machine learning techniques. In the proposed methodology, WRM efficiency is calculated by data envelopment analysis for different decision-making units (roads), and inefficient units need to be considered for further assessments. Therefore, road surface temperature is predicted by means of machine learning methods, in order to achieve efficient and effective WRM on the roads during winter in cold regions. In total, four different methods have been used to predict road surface temperature on an inefficient road. One of these is linear regression, which is a classical statistical regression technique (ordinary least square regression); the other three methods are machine-learning techniques, including support vector regression, multilayer perceptron artificial neural network, and random forest regression. Graphical and numerical results indicate that support vector regression is the most accurate method.en_US
dc.identifier.citationHatamzad, Polanco Pinerez, Casselgren. Intelligent cost-effective winter road maintenance by predicting road surface temperature using machine learning techniques. Knowledge-Based Systems. 2022;247en_US
dc.identifier.cristinIDFRIDAID 2060467
dc.identifier.doi10.1016/j.knosys.2022.108682
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.urihttps://hdl.handle.net/10037/27456
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalKnowledge-Based Systems
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleIntelligent cost-effective winter road maintenance by predicting road surface temperature using machine learning techniquesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)