The Challenge

  • High-dimensional parameter space: Calculate energy-optimized driving style taking into account topography, speed limits, timetables, preceding and following trains, etc.
  • Real-time application with limited CPU/GPU

The Approach

  • Extensive topic research, stakeholder interviews
  • Evaluate scenarios for relevance
  • Modeling train, topography and other relevant data sources
  • Design of the software architecture Two-stage algorithm development

Result & Added Value

  • An algorithm that provides energy-saving driving recommendations for the train driver
  • Driving recommendations that come very close to the real driving style of train drivers
  • Detecting implausible scenarios for special treatment
Dr. Marc GroĂźerĂĽschkamp
Head of Software & Data Technologies
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