• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 56 Issue 1
Jan.  2021
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Article Contents
LUO Wenhui, CAI Fengtian, WU Chuna, XIA Hongwen, MENG Xingkai. Text-Mining Based Risk Source Identification Model for Transportation Safety[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 147-152. doi: 10.3969/j.issn.0258-2724.20200140
Citation: LUO Wenhui, CAI Fengtian, WU Chuna, XIA Hongwen, MENG Xingkai. Text-Mining Based Risk Source Identification Model for Transportation Safety[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 147-152. doi: 10.3969/j.issn.0258-2724.20200140

Text-Mining Based Risk Source Identification Model for Transportation Safety

doi: 10.3969/j.issn.0258-2724.20200140
  • Received Date: 05 Jan 2020
  • Rev Recd Date: 08 Jun 2020
  • Available Online: 15 Sep 2020
  • Publish Date: 01 Feb 2021
  • 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%.

     

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