TTC OPERATED BY ENSCO, RAILWAY AGE OCTOBER 2025 ISSUE: Trespassing 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 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 and a quick turnaround of results.
Overview of AI and Machine Learning Solutions
AI and ML have emerged as powerful technologies for addressing complex challenges in railroad operations. These methods allow computer algorithms to continuously learn and identify patterns even within large datasets. This allows for continuous monitoring with the potential to differentiate between what is trespassing and authorized behavior. A key component in determining trespasser hotspots is the use of anonymized location data from mobile devices along the rail corridor. Reviewing this historical data along the rail corridor allows AI and ML models to distinguish patterns in human behaviors and identify areas where trespassing is likely to occur and report them as trespasser hotspot locations.
To effectively reduce trespassing incidents AI and ML models need to accurately detect when and where unauthorized access occurs along the rail corridor. Models can distinguish these patterns to determine hotspots along with other important factors like time of day, physical location, and velocity. These factors and others all play a role in using ML models to differentiate patterns and predict human behavior. Integrating location data not only enables rail operators to predict and prevent future trespassing incidents but also potentially detect near-real-time active trespassing events. This targeted approach allows for more rapid and informed decision making to proactively reduce trespassing incidents and improve railroad operations. Decreasing trespassing activity would reduce delays, allowing for increased operations and less time and money spent.
Transportation Technology Center
One limitation of using anonymous historical location data is that it is difficult to verify and be 100% certain of what is shown in the data without a ground truth analysis. A ground truth analysis is vital to test the accuracy of AI and ML models and can also be used to generate a controlled data set. To complete a ground truth assessment ENSCO researchers generated different testing scenarios at the TTC.
The TTC provided a safe and controlled space to simulate various pedestrian behaviors around railroad tracks and features. The TTC has more than 50 miles of test tracks with different railroad features that allowed the ENSCO team to gather data from a variety of realistic scenarios. During these scenarios, researchers simulated both trespassing behavior and normal human behavior to gather realistic data to test the accuracy of AI and ML models. Some of the different scenarios include unauthorized walking along the track, legal sidewalk crossing, and loitering near the track. To complete the ground truth analysis, multiple burner phones were utilized during each scenario to generate mobile device location data. This data was then evaluated by the ML models and output potential trespassing hotspots. With the new models several patterns were identified as trespassing events and ENSCO was able to validate patterns were accurately detected as trespassing.
Benefits
Implementing AI and ML solutions for detecting railroad trespassers offers substantial benefits, with significant potential to improve overall railroad operations. AI and ML models enable railroad personnel to intervene proactively rather than reacting after incidents occur, potentially reducing the number of future trespassing incidents. By improving hotspot detection, rail operators can strategically allocate resources to areas of greatest concer, which can minimize spending and maximize resources. Such targeted interventions have the potential to significantly decrease the number of trespassing incidents, resulting in fewer delays and downtime on rail property.
In rural areas, where monitoring large expanses of track is challenging due to limited resources, these new models can provide efficient surveillance over extended distances allowing railroad operators to allocate more time to day-to-day operations. In densely populated urban settings, advanced analytics can distinguish normal, everyday movement from genuine trespassing threats, minimizing false alarms and improving enforcement efforts. Overall, integrating AI and ML methods into railroad trespassing detection will not only benefit rail operators but lead to a safer rail environment.
At the same time, privacy considerations and data security remain at the forefront of this research, requiring constant vigilance to ensure anonymity and compliance with evolving privacy standards. The significant benefits provided by the advanced ML models in trespasser detection—faster response times, targeted resource allocation, reduced financial loss, and improvement to railroad operators’ mental health—all highlight the importance of continued innovation in railroad safety.
What’s Next?
While these new methods have shown substantial promise in improving railroad safety, ongoing research and development remain essential. Future efforts will extend these innovative technologies from historical analysis and near real time reporting to actively detecting trespassing events as they occur. Creating a user interface that allows railroad operators to investigate hotspots in more detail to suggest prevention infrastructure. Furthering analysis can be done to evaluate after a trespassing hotspot has been identified and prevention methods have been put in place to see if trespassing has decreased in that area. AI and ML models will continue to improve over time and railroad operations will improve with it.
