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Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis

机译:基于粗糙关系分析的交通事故事故成因分析。

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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.
机译:这项研究的目的是表明,从粗糙集理论生成的决策规则可以用于新的关系分析。粗糙集理论通常对小数据集比对大数据更有效。如果我们能够处理决策规则及其复杂性,那么仍然可以使用粗糙集理论来分析大数据。这就是为什么作者在这项研究中提供了一种统计方法来逾期属于大数据的问题。根据统计方法,由粗糙集理论产生的许多决策规则成为有用的信息。使用2013年在美国发生的交通事故的真实案例数据,本文发现了事故因果之间的关系,这些关系可能会被交通领域的决策者参考。

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