基于文本挖掘的道路运输安全风险源辨识模型

为了解决当前道路运输安全风险源辨识工作中数据短缺和人员工作量较大的问题，从文本挖掘的角度出发，提出一种能够自动辨识道路运输过程中安全风险源的模型. 该模型首先对道路运输文本进行因果句提取，并对因果句进行分词操作，实现安全风险源特征的增强；其次，进行适应卷积神经网络（convolutional neural networks，CNN）输入的、包含词信息和位置信息的特征构造；然后，将特征构造的结果输入到CNN实现安全风险源的辨识；最后，利用道路交通事故报告进行实验. 实验结果表明:提出的辨识模型能辨识大部分的道路运输安全风险源因素，准确率约为77.321%. 

Text-Mining Based Risk Source Identification Model for Transportation Safety

In order to solve data deficiency and excessive staff workload in the risk-source identification of road transportation safety, an automatic identification model is proposed from the angle of text mining. Firstly, the model performs feature enhancement preprocessing operation through the causality sentence extraction and extracted sentence segmentation. Secondly, the feature construction adapted to the convolutional neural network (CNN) is conducted, which contains word information and position information. Thirdly, the results of feature construction feed into the CNN to realize the identification of risk sources. Finally, experiments are conducted with the data sets of traffic accidents, demonstrating that the proposed model can identify most of risk sources for road transportation safety with the accuracy of about 77.321%.