A recent industry report (PDF) claims the UK rail network could build on the 1.6 billion passenger rail journeys recorded by the end of March 2024 and carry a further 1 billion journeys by the mid-2030s. The rise of digital systems, data, and interconnected suppliers will likely create even more points of failure, requiring a combination of complexity and control over the next decade.
A central theme of the report is that AI will become the operating system of modern railways, used not as a central collection of models and algorithms but as a layer of prediction, optimization and automated monitoring found in infrastructure, rolling stock, maintenance yards and stations (pp.18-23). Rather than completely replacing human activity, this technology channels human focus within the daily work schedule.
Make maintenance predictive and data-driven
Traditional railway maintenance relies on fixed schedules and manual inspections, which are reactive and labor-intensive. The white paper states that Network Rail relies on engineers walking the tracks to find defects (p.18). AI is moving the industry toward predictive maintenance, analyzing data from sensors to predict failures before they cause significant disruption.
This includes a combination of sensors and imaging such as high-resolution cameras, LiDAR scanners, and vibration monitors. These provide data to machine learning systems that can signal deterioration of lines, signals, and electrical equipment before failure (pp.18-19).
These monitoring programs can generate alerts months in advance and reduce emergency calls. The timeframe for predicting asset failure varies by asset type. Network Rail’s approach to intelligent infrastructure must move from ‘discover and fix’ to ‘predict and prevent’.
While Network Rail focuses on data-driven maintenance and tools designed to integrate asset information, European R&D programs (such as Europe’s Rail and its predecessor Shift2Rail) are funding projects like DAYDREAMS, which are similarly aimed at prescriptive asset management. Forecasting at scale requires a common approach to achieving transformation.
Traffic control and energy efficiency
Optimizing operations beyond predictive maintenance provides significant benefits. AI systems use live and historical service data, such as train positions, speeds, and weather forecasts, to predict disruptions and adjust traffic flow. Trials of digital twins and AI-based traffic management in Europe, in parallel with research and testing of AI-assisted driving and positioning, have the potential to increase overall network capacity without laying more tracks (p.20).
The algorithm also advises drivers on optimal acceleration and braking, potentially saving energy by 10-15%. Energy savings grow quickly across large networks when route variations, traction, and timetable constraints are taken into account.
Safety surveillance and CCTV
Visible AI applications focus on safety and security. Obstacle detection uses thermal cameras and machine learning to identify hazards that are invisible to the human eye. AI also monitors railroad crossings and analyzes surveillance camera footage to spot abandoned items and suspicious activity (pp. 20-21). For example, AI and LiDAR are being used for crowd monitoring in London Waterloo as part of a suite of safety tools.
Passenger flow and journey optimization
AI can use ticket sales, events and mobile signals to predict demand, allowing operators to adjust vehicle numbers and reduce overcrowding, the report said. Passenger counting is a high-impact, low-impact application. Better data supports better timetables and clearer customer information.
Cyber security issues
As operational technology converges with IT, cybersecurity becomes a critical operational issue. Legacy systems without a replacement plan pose risks, as does modern analytics and integrating older infrastructure. This creates an attractive situation for attackers.
The future of AI in rail includes sensors that operate in extreme environments, models trusted and tested by operators, and governance that treats cyber resilience as inseparable from physical safety. The report’s message is that AI will be here anyway. The question is whether railroads will actively adopt and control it, or inherit it as an unmanaged complexity.
(Image source: “Trainjunction” by jcgoble3 is licensed under CC BY-SA 2.0.)
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