Welcome TTC Conference Attendees!
Here you will find ideas and resources demonstrating how advanced analytics can help maximize rail life and reduce fuel consumption. Whether using data from a TTC test, a controlled field experiment, or through routinely collected rail measurement data, AI and mathematical optimization can reduce wheel wear and improve asset utilization.
Analysis and models derived from data generated in TTC tests
These reports are examples of how statistical and mathematical modeling can be applied to data produced in a TTC test. While the focus was on fuel conservation, by nature, rail and wheel wear is also impacted, and the same tools can focus on wear.
Analysis of the Energy Reduction Benefits of MPL NatureBlend Locomotive Wheel Flange Lubricant
We analyzed test data generated at the Transportation Technology Center for our client, MPL Innovations.
Projecting the Energy Reduction Benefits of MPL NatureBlend Locomotive Wheel Flange Lubricant to Specific Railroad Profiles
We analyzed and projected test data generated at the Transportation Technology Center for our client, MPL Innovations.
Additional Resources for Fuel Conservation and GHG Emmisions Reduction
Interested in Safety?
Check out our Safety Analytics for Railroads offerings.
Connect with the experts on LinkedIn:
Wayne Kennedy | Rail Sustainability Consultant
Rob Stevens | Analytics and Machine Learning
If you would like more information, please complete the form below or contact us directly here.