基于主题提取模型的交通违法行为文本数据的挖掘

Text data of traffic illegal acts mining based on latent dirichlet allocation model

长期以来,各类交通事故严重影响了人们生命财产安全和社会经济发展。交通事故分析是对交通事故资料进行调查研究,发现事故动向和各种影响因素对事故总体的作用和相互关系,以便定量地认识事故现象的本质和内在规律。通过对交通事故中记录驾驶员违法行为的文本数据进行分析,提出了一种文本主题提取模型和技术,来挖掘交通事故中驾驶员风险驾驶因素,解决以往交通事故统计中交通违法行为难以挖掘的问题,计算出影响交通事故的最大支配因素。最后以北京地区一般程序处理的交通事故为例,结合北京市交通管理专家经验,验证该模型可应用于交通事故中违法行为的主题提取,结论与长期治理经验相吻合。

For a long time,all kinds of traffic accidents have seriously affected people′s life,property safety and social and economic development.Traffic accident analysis is the investigation and study of traffic accident data.It finds out the pattern of accident trends and various influencing factors on the overall accidents and researches the relationship between them,so as to quantitatively understand the nature and internal law of accident phenomena.Based on the analysis of the text data recorded in traffic accidents,this paper proposes a text topic extraction model and technology to find drivers′risk factors in traffic accidents,in order to solve the problem that traffic violations are difficult to excavate in the past,and to calculate the most dominant factors that affecting traffic accidents.Finally,taking the traffic accidents in Beijing as an example,combining with the experience of traffic management experts,the effectiveness of the proposed model is verified.It turns out that the model is valid,and the conclusion with using it is consistent with the long-term management experience.