TTC OPERATED BY ENSCO
IMPROVING RAIL OPERATIONS USING AI AND MACHINE LEARNING
DETECTING TRESPASSER HOTSPOTS
TTC Operated by ENSCO
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BY SAMANTHA KIRKPATRICK, SENIOR DATA SCIENTIST, ENSCO, INC.
respassing along a rail corridor remains one of the most important and challenging issues that railroad operators face every day. Trespassing on railroads has resulted in many unfortunate incidents that can have significant impact on railroad operations including trauma to the crew, time delays, and financial loss. Currently there are methods in place by rail operators to help in reducing the number of incidents, such as physical barriers and signage. However, these conventional methods have limitations, as they are
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costly, often incomplete, and difficult to maintain. In recent years there have been many improvements in technology with artificial intelligence (AI) and machine learning (ML) methodologies. These new methodologies are not only capable of expanding upon the current methods mentioned above by potentially pinpointing areas of focus, but they also offer promising new solutions to improve safety in railroad operations and reduce trespassing incidents by predicting where trespassing is likely to occur. By examining anonymized location data from devices operating
within geofenced rail corridors, areas can be identified where users are suspected to “linger,” which often indicates higher regions of trespassing, or trespassing hotspots. Through a project with the Federal Railroad Administration (FRA), ENSCO utilized the Transportation Technology Center (TTC) in Pueblo, Colo., to explore how new AI and ML methods can be used to help reduce trespassing incidents and reduce operational risk. The TTC offers the ability to test multiple different safety scenarios in one controlled space. On the ground testing of Al models allows for easy adjustments October 2025 // Railway Age 35