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Making A Railroad’s Employees Safer – An Open Source Case

The Opportunity

  • After seeing the success of our project at another railroad, this company asked us to do “exactly what you did with them for us.”
  • The company had recently completed setting up a “big data” platform and desired this application to be the first one to use that platform.

Our Approach

As in the other case, we applied advanced analytics and predictive models to:

  • identify risk factors;
  • evaluate the effectiveness of safety programs and processes;
  • predict which employees, at a day-shift level, were most at risk for an accident or injury.

The data processing and modeling were developed and deployed in a Spark environment, with PySpark and R as the principal tools.  As is usual with our collaborative model, we worked with the company’s data science team with these tools.

The Impact

  • The pilot project assessed risk for one type of operational employees.  The results suggested overall policy changes that resulted in a 40% reduction in severe injuries in the first five months.  Seeing these stunning results, they accelerated the project, expanding to other employee types.
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