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

• Main Authors: Zeng Xiangkun, Zhang Junhui, Shi Tuo, Shao Kejia
• Format: Article
• Language: zho
• Published: National Computer System Engineering Research Institute of China 2019-06-01
• Series: Dianzi Jishu Yingyong
• Subjects: traffic accident; driving risk; text mining; factor analysis
• Online Access: http://www.chinaaet.com/article/3000103393

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.