Data mining on Chinese train accidents to derive associated rules

The costs of fatalities and injuries from train accidents have a great impact on society. As part of the authors' effort to understand the characteristics of past train accidents, this paper presents an analysis of significant train accidents occurring in China from 1954 to 2014. Rough set theory and associated rules approaches are applied in analyzing the collected data. The results show that although most derived rules are unique, some rules are worth noting. Collision accidents generally lead to more casualties than derailment accidents, and the most frequent cause of accidents is human error. Additionally, most train accidents occur during summer. These findings can provide railway leaders with lessons and rules learned from past accidents, thus facilitating the establishment of a safer railway operation environment in China.

• Record URL:
  • https://doi.org/10.1177/0954409715624724
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544097
• Supplemental Notes:
  • © IMechE 2016.
• Authors:
  • Chen, Dingjun
  • Xu, Chang’an
  • Ni, Shaoquan
• Publication Date: 2017-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 239-252
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
  • Volume: 231
  • Issue Number: 2
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4097
  • EISSN: 2041-3017
  • Serial URL: http://pif.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Crash analysis; Crash data; Data mining; Railroad crashes; Railroad safety
• Uncontrolled Terms: Rough set theory
• Geographic Terms: China
• Subject Areas: Data and Information Technology; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01707012
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2019 4:37PM