关联规则挖掘在道路交通事故分析中的应用

从大量的交通事故数据中找出引发交通事故的关键因素是提高道路安全水平的重要手段。基于某市全年的交通事故数据，采用改进的Apriori算法挖掘出强关联规则，通过一个新的相关性度量——相关值对关联规则进一步筛选，从中找出各因素对交通事故的影响规律。结果表明，该方法可以一定程度上提高关联规则挖掘的效率，并能够量化事故原因和事故结果之间的相关程度，从而找出有价值的规则。本文的研究方法和结果可以为相关交通管理部门提供决策支持。

Application of Association Rules Mining to Traffic Accidents Analysis

It is an important measure to find the key factors that cause traffic accidents from a large number of traffic accident data in order to improve safety of road. Based on the traffic accident data of a city in the whole year, the improved Apriori algorithm was used to mine the strong association rules and a new dependence measure-correlation was adopted to further improve the association rules. Then the influence law of each factor on traffic accidents was found out. Experimental results show that the method can improve the efficiency of association rule mining, and quantify the correlation between the cause of the accident and the result of the accident, so as to find out the valuable rules. The study methods and results can provide decision support for relevant traffic management departments.