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dc.contributor.authorGeçer Sargın, Feral
dc.contributor.authorDuvarcı, Yavuz
dc.contributor.authorİnan, E.
dc.contributor.authorKumova, Bora İsmail
dc.contributor.authorAtay Kaya, İlgi
dc.date.accessioned2017-02-24T13:46:32Z
dc.date.available2017-02-24T13:46:32Z
dc.date.issued2011
dc.identifier.citationGeçer Sargın, F., Duvarcı, Y., İnan, E., Kumova, B. İ., and Atay Kaya, İ. (2011). A data coding and screening system for accident risk patterns: A learning system. WIT Transactions on the Built Environment, 116, 505-516. doi:10.2495/UT110431en_US
dc.identifier.isbn9781845645205
dc.identifier.issn1743-3509
dc.identifier.urihttp://doi.org/10.2495/UT110431
dc.identifier.urihttp://hdl.handle.net/11147/4908
dc.description17th International Conference on Urban Transport and the Environment - UT 2011; Pisa; Italy; 6 June 2011 through 8 June 2011en_US
dc.description.abstractAccidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called 'accident combination', reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir's case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. © 2011 WIT Press.en_US
dc.description.sponsorshipThe Scientific and Technological Research Council of Turkeyen_US
dc.language.isoengen_US
dc.publisherWIT Pressen_US
dc.relation.isversionof10.2495/UT110431en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTraffic accidentsen_US
dc.subjectLearning systemsen_US
dc.subjectData miningen_US
dc.subjectSimilarity indexen_US
dc.titleA data coding and screening system for accident risk patterns: A learning systemen_US
dc.typeconferenceObjecten_US
dc.contributor.authorIDTR115768en_US
dc.contributor.authorIDTR117217en_US
dc.contributor.authorIDTR115745en_US
dc.contributor.iztechauthorGeçer Sargın, Feral
dc.contributor.iztechauthorDuvarcı, Yavuz
dc.contributor.iztechauthorKumova, Bora İsmail
dc.contributor.iztechauthorAtay Kaya, İlgi
dc.relation.journalWIT Transactions on the Built Environmenten_US
dc.contributor.departmentIzmir Institute of Technology. City and Regional Planningen_US
dc.identifier.volume116en_US
dc.identifier.startpage505en_US
dc.identifier.endpage516en_US
dc.identifier.scopusSCOPUS:2-s2.0-84875017141


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