Britain’s railway network is entering a defining decade. According to a recent industry report, rail could support an additional one billion passenger journeys by the mid-2030s, building on the 1.6 billion trips recorded in the year ending March 2024. Reaching that milestone, however, will depend less on laying new track and more on how effectively the industry uses data, digital systems, and artificial intelligence.
The report paints a picture of a rail network that is both more capable and more complex. As sensors, software platforms, and interconnected suppliers multiply, so do the potential points of failure. The central challenge for the next decade will be balancing this complexity with greater control.
AI as the operating layer of modern rail
Rather than a single, all-encompassing system, the report argues that AI will function as a distributed operating layer across the rail network. These systems will be embedded in infrastructure, trains, maintenance depots, and stations, quietly handling prediction, optimisation, and automated monitoring.
Crucially, AI is not presented as a replacement for people. Instead, it is designed to guide human attention—helping engineers, controllers, and operators focus on the most critical issues during increasingly busy daily schedules.
From reactive fixes to predictive maintenance
One of the most significant shifts will be in maintenance. Today, much of rail maintenance remains reactive, relying on fixed inspection schedules and manual checks. The report highlights how engineers still walk large sections of track to identify defects, a process that is time-consuming and labour-intensive.
AI-driven predictive maintenance aims to change that model entirely. Sensors and imaging technologies, including high-definition cameras, LiDAR scanners, and vibration monitors, continuously collect data on tracks, signals, and electrical assets. Machine-learning systems then analyse this information to detect early signs of wear or failure.
In some cases, these systems can flag issues months in advance, allowing planned repairs instead of emergency call-outs. The precise prediction window varies by asset type, but the overall goal is clear: move from a “find and fix” approach to one focused on “predict and prevent.”
Network Rail has already emphasised the importance of data-led maintenance, while European research programmes such as Europe’s Rail are funding similar initiatives. The report notes that real transformation will depend on adopting common standards and approaches across the network.
Smarter traffic control and lower energy use
Beyond maintenance, AI has the potential to significantly improve day-to-day operations. By combining live and historical data—such as train locations, speeds, and weather forecasts—AI systems can anticipate disruption and dynamically adjust traffic flow.
Trials across Europe using digital twins and AI-based traffic management suggest that overall network capacity could be increased without adding new infrastructure. AI-assisted driving tools and more accurate train positioning are also being tested as ways to smooth operations on busy routes.
Energy efficiency is another area of opportunity. Algorithms can advise drivers on optimal acceleration and braking patterns, taking into account gradients, traction types, and timetable constraints. The report estimates potential energy savings of 10% to 15%, which can quickly add up across a national network.
Enhancing safety and security
Some of the most visible uses of AI are already appearing in safety and security systems. Obstacle detection using thermal cameras and machine learning can identify hazards that are difficult for the human eye to spot, particularly in poor visibility.
AI is also being applied to level crossings and CCTV monitoring. Systems can analyse video feeds to identify unattended items, unusual behaviour, or overcrowding. At London Waterloo, for example, AI and LiDAR technology are used to monitor crowd movements as part of a broader safety toolkit.
Improving passenger flow and the travel experience
AI’s role extends beyond infrastructure and operations into the passenger experience. By analysing ticket sales, major events, and anonymised mobile data, AI models can forecast demand more accurately. This allows operators to adjust carriage numbers, reduce overcrowding, and improve timetable planning.
Passenger counting may not grab headlines, but the report describes it as one of the most effective applications of AI. Better data supports clearer customer information and more reliable journeys, outcomes that directly affect public confidence in rail travel.
Cybersecurity moves to the forefront
As rail systems become more digital, cybersecurity emerges as a critical concern. The convergence of operational technology and IT systems increases exposure to cyber threats, particularly where legacy systems remain in use without clear upgrade paths.
Integrating modern analytics with older infrastructure can create vulnerabilities that are attractive to attackers. The report argues that cyber resilience must be treated as inseparable from physical safety, with governance frameworks that reflect this reality.
