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.
We analyzed test data generated at the Transportation Technology Center for our client, MPL Innovations.
We analyzed and projected test data generated at the Transportation Technology Center for our client, MPL Innovations.