基于AHP和混合Apriori-Genetic算法的交通事故成因分析模型

Traffic accident causation analysis model based on AHP and hybrid Apriori-Genetic algorithm

针对交通事故数据多维多层的特点,对交通事故的主要成因与潜在规律进行了研究。从驾驶员、车辆、时间—地点、环境四个维度出发,提出了基于层次分析法(AHP)和混合Apriori-Genetic的模型挖掘事故成因。首先,引入AHP对事故诱发因素进行重要度排序,在客观分析的基础上将事故因素量化,筛选出引发交通事故的主要因素;其次,结合混合的Apriori和遗传算法对主要因素进行定向分析,找出关联规则,提高挖掘的准确性。相关对比实验的结果表明该模型可以减少无用规则的产生并提高挖掘的准确性,具有一定的科学意义和应用价值。

In view of the characteristic of multi-dimensional and multi-layer in traffic accident data, this paper proposed a new model to research the main reasons and potential rules in traffic accidents. The model started from the four main dimensions such as the drivers, the vehicles, the time-address and the environment, and used a way which based on AHP and hybrid Apriori-Gentic algorithm to mine causes of accident. First of all, the AHP sorted the importance of the influencing factors about accident. Then on the basis of objective analysis, the model quantified the influencing factors and selected the main influencing factors. Finally the model combined the genetic algorithm with the Apriori to directional analyze the main influencing factors and find the association rules out. The experimental result shows that the model can reduce the generation of useless rules and improves the accuracy of mining, which has certain scientific significance and application value.