@article{
 title = {Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 pages = {60-71},
 volume = {3},
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 month = {7},
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 accessed = {2017-04-13},
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 last_modified = {2018-01-17T09:19:09.300Z},
 read = {false},
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 citation_key = {Erden2016c},
 folder_uuids = {c13dd18d-72bc-4e9b-95ac-d2413767a57f,087a4c18-2d08-4023-bebf-79183c51a65a,3bfea2f8-faec-462e-ba9b-5d07362117cf,e2d35d93-b411-400d-b739-0930b48e0e90},
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 abstract = {The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.},
 bibtype = {article},
 author = {Erden, Caner and Çelebi, Numan},
 journal = {International Journal of Rough Sets and Data Analysis},
 number = {3}
}

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