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Mining aberrant driving behavior characteristics

Moustakis Vasilis, Tsironis Loukas, Kontogiannis Thomas, H. Mavropoulos

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URIhttp://purl.tuc.gr/dl/dias/1FA4C9A6-4931-4606-901C-F7E43F935E59-
Languageen-
TitleMining aberrant driving behavior characteristicsel
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
CreatorTsironis Loukasen
CreatorΤσιρωνης Λουκαςel
CreatorKontogiannis Thomasen
CreatorΚοντογιαννης Θωμαςel
CreatorH. Mavropoulosen
Content SummaryThis article demonstrates the contribution of machine learning in modeling of aberrant driving characteristics. We demonstrate how machine learning can be deployed to identify, classify, and model interesting and useful knowledge patterns, which are hidden in self reported replies to a Driving Behavior Questionnaire. Demonstration is based on empirical results drawn from 1453 responders in Greece. Experimentation was based on two machine learning approaches: decision tree learning and association rule mining and learning focused on the exact driver characteristics thought to be responsible for an accident to occur. Accuracy of results was assessed using randomized testingen
Type of ItemΣύντομη Δημοσίευση σε Συνέδριοel
Type of ItemConference Short Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-05-
Date of Publication2005-
Bibliographic CitationL. Tsironis, H. Mavropoulos, V. Moustakis, T. Kontogiannis, "Mining Aberrant Driving Behavior Characteristics, WSEAS Transactions on Information Science and Applications," στο διεθνές συνέδριο WSEAS, Κέρκυρα, Ελλάδα, 2005.el

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