基于粗糙关联分析法的交通事故成因分析

C. Erden, N. Çelebi
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引用次数: 27

摘要

本研究的目的是证明由粗糙集理论生成的决策规则可以用于一种新的关联分析。粗糙集理论通常更适用于小数据集而不是大数据。如果我们能够处理决策规则及其复杂性,那么用粗糙集理论分析大数据仍然是可能的。因此,在本研究中,作者对属于大数据范畴的逾期问题提出了一种统计方法。根据统计方法,粗糙集理论生成的大量决策规则成为有用的信息。本文利用2013年美国发生的一起交通事故的真实案例数据,发现事故成因之间的关系,为交通领域的决策者提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis
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.
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