A recent industry report released in September 2025 projects that Britain’s railway system could accommodate an additional one‑billion passenger journeys by the mid‑2030s, building on the 1.6 billion journeys recorded up to the end of March 2024. The study, which draws on data from Network Rail and European research programmes, argues that the next decade will see a blend of increased complexity and tighter control as digital systems, data analytics and interconnected suppliers expand the potential for operational failure.
Predictive, data‑driven maintenance
Traditional rail maintenance in the UK has relied on fixed schedules and manual inspections, a reactive and labour‑intensive approach. The report notes that Network Rail still depends on engineers walking the track to spot defects. It proposes a shift to predictive maintenance, where sensors and imaging devices—high‑definition cameras, LiDAR scanners and vibration monitors—feed machine‑learning models with real‑time data. These models can flag degradation in track, signalling and electrical assets months before a failure occurs, thereby reducing emergency call‑outs and shifting the focus from “find and fix” to “predict and prevent.” Network Rail’s intelligent infrastructure initiatives are expected to consolidate asset information and support this transition. European research programmes such as Europe’s Rail and its predecessor Shift2Rail fund projects like DAYDREAMS, which aim at prescriptive asset management. The report stresses that large‑scale prediction requires a common approach to achieve transformation.
Traffic control and energy efficiency
Beyond maintenance, operational optimisation offers substantial returns. AI systems can analyse live and historical operating data—including train positions, speeds and weather forecasts—to anticipate disruptions and adjust traffic flow. Trials of digital twin and AI‑based traffic management in Europe, together with research into AI‑assisted driving and positioning, could increase overall network capacity without the need for additional track. Algorithms that advise drivers on optimal acceleration and braking could save 10–15 % of energy. When applied across a large network, these savings compound quickly, offering both economic and environmental benefits.
Safety monitoring and CCTV
Visible AI applications in rail safety focus on obstacle detection and security. Thermal cameras and machine‑learning models can identify hazards beyond human visibility. AI also monitors level crossings and analyses CCTV footage to spot unattended items and suspicious activity. For example, AI and LiDAR are employed for crowd monitoring at London Waterloo as part of a broader suite of safety tools.
Passenger flows and journey optimisation
AI can forecast demand by analysing ticket sales, event schedules and mobile signals, enabling operators to adjust the number of carriages and reduce overcrowding. Passenger counting, a high‑impact but low‑drama application, provides better data for timetable planning and clearer customer information.
Cybersecurity challenges
As operational technology converges with information technology, cybersecurity becomes a critical operational issue. Legacy systems that lack replacement plans pose a risk, as does the integration of modern analytics with older infrastructure. These conditions create attractive targets for attackers. The report argues that the future of AI in rail will involve sensors operating in extreme environments, models that are trusted and tested by operators, and governance that treats cyber resilience as inseparable from physical safety. AI will arrive regardless; the question is whether railways adopt and control it proactively or inherit it as unmanaged complexity.
Implications and next steps
The report’s findings suggest that the UK rail sector is on the cusp of a technological transformation that could significantly increase capacity and efficiency. Network Rail’s ongoing investment in intelligent infrastructure and the European research programmes’ focus on predictive maintenance and traffic optimisation are expected to drive this change. Over the next few years, the industry will need to address cybersecurity risks associated with the integration of legacy systems and new analytics platforms. If these challenges are managed effectively, the rail network could deliver the projected additional capacity by the mid‑2030s, enhancing mobility for millions of passengers.
