Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 116-122.DOI: 10.3778/j.issn.1002-8331.2010-0269

Previous Articles     Next Articles

Visual Mining and Analysis Method of Text Data in Traffic Accident

CHENG Yuhang, ZHANG Jianqin, LI Jiangchuan, ZHANG An   

  1. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • Online:2021-11-01 Published:2021-11-04



  1. 北京建筑大学 测绘与城市空间信息学院,北京 100044


In order to deeply analyze the spatiotemporal characteristics and causative factors, which hide in the text data of safety production in transportation industry, select features and causative words in relevant papers as corpus, using Word2vec to construct the vector model of traffic accident words, classifying the key words of Beijing safety production event in traffic industry by using Sigmoid function, two kinds of keywords including spatiotemporal characteristics and causal factors are obtained, using the Gephi and Neo4j to visually analyze the feature keywords, through the summary of causative theme, analyse causal factors keywords. The result shows that traffic accidents mainly occurred in the third quarter, and the total number of accidents in the six urban areas of the center is much higher than that in other urban areas, the proportion of casualties in other urban areas is higher. Human being, equipment and environmental factors are the main causes of traffic accidents. Based on the above analysis, the paper puts forward reasonable suggestions to provide information support and scientific guidance for the relevant management departments of safety production in Beijing transportation industry.

Key words: text data, safety accidents in transportation, word vector, keyword classification, visual analysis



关键词: 文本数据, 交通安全事故, 词向量, 关键词分类提取, 可视化分析