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Reviewing the Use of Proactive Data Analysis in Developing Rail Safety Culture

[+] Author Affiliations
Greg Placencia

University of Southern California, Los Angeles, CA

Paper No. JRC2016-5799, pp. V001T06A015; 7 pages
  • 2016 Joint Rail Conference
  • 2016 Joint Rail Conference
  • Columbia, South Carolina, USA, April 12–15, 2016
  • Conference Sponsors: Rail Transportation Division
  • ISBN: 978-0-7918-4967-5
  • Copyright © 2016 by ASME


While experience is often the best teacher, learning from precursors is much less painful. The aviation and health care industries have greatly benefited from proactively analyzing and developing measures to address sentinel events and learning from various data sources. Such reflective learning is typical of High Reliability Organizations (HROs) with strong learning cultures. As technology like Positive Train Control increasingly integrates into the rail industry, the resulting data they inevitably produce can provide a wealth of knowledge that can greatly improve safety if the data streams are well managed and not blindly mined. For example, simulators generate data while locomotive engineers use them. During training, such data can indicate weak points where the engineer can improve. Examining such data over multiple engineers can establish general areas of strengths and weaknesses among trainees where instructors can place more or less focus and develop better overall training options. Such data could potentially be used to improve cab design and establish how trains and cab care would operate along a given rail line. This paper will explore the use of data streams from various sources, including those currently used like injury reports, emerging ones like simulation training evaluations and data logs to develop better safety cultures within the rail industry.

Copyright © 2016 by ASME
Topics: Safety , Rails



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