Significant contributions to 2023 freight rail safety and performance advances.
"The technology teams at Railinc leveraged their expertise this past year to create solutions and improve products that made the North American freight rail industry safer, more efficient, and more competitive."
Joan Smemoe
CIO and VP of Information Technology
500+
Line of road failures prevented
9500
EHMS detectors reporting to Railinc
10000
automatically identified stopped trains for remediation in Chicago through Railinc’s Clear Path tool
16000
train braking scenarios run per hour with PTC
Data science and data platform team in 2023
Utilizing Wheel Temperature Data
An industry Technical Advisory Group continues to examine wheel temperature data to improve monitoring technology and data sharing. The current focus looks to complement previous work to clearly identify trains in a non-braking status. With non-braking trains identified, a secondary analysis identifies wheels that are warmer than expected given that the train is in a non-braking configuration. Part of this effort involved the use of the DBSCAN clustering algorithm, which is applied to several dimensions of wheel temperature data to identify statistically anomalous railcars in terms of their wheel temperature profiles. Once the alerts are received, cars are pulled for inspection or repair, which would make freight rail safer and would cut down on more costly repairs.
Advancing Analytical Functions of the TransmetriQ Rail Management Platform
Our data science team worked with the TransmetriQ Platform to advance its functionality and improve visibility for shippers. The ETA service was modified this year to expand its window of knowledge from two years to four, and now updates car history data every 24 hours. Improvements included providing customers with better history on car movements and trips to give a more detailed cycle time and equipment utilization reporting. Once the accurate data was integrated into the platform, the TransmetriQ team was able to transform the data into a modern user interface.
Defining Relationships in Railway Movement Data
Understanding relationships between various aspects of rail shipping – distance between destination and origin, how many cars have traveled a road, and road marks, to name a few – is critical for better visibility into ETA. By using a graph database with current and historical track data, and referencing current waybills, we can provide solutions to common issues: if a train is alerting as off-route, out-of-order events at railyards, or identifying anomalous movements. The algorithm in the graph database pairs one aspect to another to define a relationship between them. In one real-world scenario, a car owner noticed their car was off-route and sent in a request for remediation. The off-route alert was found to be a waybill recalculation, based on a bridge outage that was reflecting in the graph database due to previous trains routing around the area for an extended period.