The rail sector is undergoing a digital transformation, with various digital technologies being adopted to enhance operational efficiency, improve customer experience and increase safety and sustainability
These strategies are helping modernise infrastructure, optimise train services and address the growing demands of the network.
Digital twinning is one of the innovations rapidly gaining traction, offering a powerful way to simulate, monitor, and optimise various aspects of railway operations, infrastructure, and assets. It is a virtual representation of a physical asset or system that is continually updated with real-time data from sensors and Internet of Things (IoT) devices. This enables operators to visualise, analyse, and optimise operations more efficiently.
Introduction of digital twins to the industry has opened opportunities for collaboration and innovation throughout the transport system. Sharing data and insights amongst stakeholders enables train operators, maintenance providers and technology vendors to foster greater transparency, efficiency and innovation.
The Department for Transport recently published a report that found integrating transport networks using digital twins could amount to £850 million in monetised benefits over the next ten years. Five priority use cases were used in the study: network capacity management; multimodal journey optimisation; integrated incident and emergency management; planned works and maintenance management; and freight management at ports. While the concept of digital twins is not entirely new, recent advancements in sensors, data and analytics have elevated its potential in rail.
Usage cases
Digital twins can be used to create virtual replicas of railway tracks, stations and other infrastructure by connecting these models to real-time data using sensors embedded in the physical asset. The sensors can detect track wear, alignment problems and corrosion. By continuously monitoring the health and behaviour of critical assets, operators can anticipate maintenance needs before they cause disruption. Predictive maintenance can therefore reduce downtime, improve reliability and save money from costly fixes.
Digital twins can enable operators to optimise train scheduling and routeing by simulating different scenarios and analysing data in a digital environment. Operators can identify opportunities to streamline operations, identify bottlenecks, optimise train flows and maximise capacity. Using digital twins in this way can improve network efficiency and punctuality and reduce congestion and carbon emissions.
Using real-time insights into the performance of individual assets enables operators to monitor and manage fuel consumption, engine health and wear on brakes and wheels. As with infrastructure monitoring applications, forecasting potential failures and maintenance based on performance data can provide insight into when to replace and repair trains before breakdowns, supporting the reliability of train services.
Digital twins can play a crucial role in safety and security across the rail sector. Operators can use digital twin simulations to better prepare for accidents, derailments and other critical incidents such as extreme weather events. These simulations can guide decisions on how to improve safety protocols and infrastructure resilience.
Security systems can be enhanced by integrating real-time data from CCTV cameras and sensors, so operators can detect potential security threats, while simulations used in training can enable staff to respond effectively to emergencies.
In stations and terminals, digital twins can track passenger flow in real-time, helping to manage congestion during peak hours or in case of delays. Resources can be appropriately allocated at busier times, with signage and train arrivals adjusted accordingly. Predictive crowd management can also help identify how passengers move through a station, helping to model efficient train station design.
Implementation of digital twins
Implementation of digital twins in the rail industry is already underway with key organisations actively adopting the technology across several projects demonstrating positive results.
Network Rail announced in 2021 its vision for future procurement, stating: “A data-driven synthetic environment will provide the opportunity we need to break away from highly customised and bespoke projects and products, integrate our processes, and deliver faster, safer, and more reliable schemes.” Since then, Network Rail has called on its supply chain to develop the building blocks required to make the virtual design platform a reality.
Network Rail used digital twins for the Transpennine Route Upgrade (TRU), a multibillion-pound railway upgrade programme aiming to modernise the 100-km rail line, increase capacity and shorten travel time for passengers. The objective was to generate a route-wide digital twin to deliver digital engineering and asset management for the TRU. Digital twins were also used to improve data accessibility and deliver the highest quality information ever for a major railway upgrade. The TRU’s digital twin enabled team members to identify potential clashes early and optimise construction scheduling. The high quality data also enhanced efficiency and enabled better decision-making, reducing cost and risk.
Digital twin technology was tested in the design, construction and operational management of the High Speed Rail network. AI and digital models were used to drive forward the maintenance and renewals planning process and showcased how the technology can be used to make rail operations, maintenance and renewals more efficient. HS1’s use of digital twins included creating detailed 3D models of the entire rail corridor, including stations, tunnels, viaducts and tracks. The project has acted as a catalyst for improvement in efficiency across the rail network.
The London Underground is one of the world’s oldest and busiest metro systems. TfL created a virtual replica of the underground network to gain valuable insight into the condition of tunnels, tracks and stations in real time, enhancing management and maintenance across the system. TfL also utilised digital twins to optimise train schedules, improve passenger flow and enhance safety and security measures.
The RSSB supports the rail industry in the UK with safety, standards and innovation and is involved in the development and promotion of digital twin technology to improve rail operations. The organisation developed a risk model outlining the risks to the network in extreme rainfall. Using digital twins, the RSSB was able to gain granular weather forecasts, which were fed into the digital model. The data was then able to predict earthworks failures and provide insight on the use of optimum speed. Network Rail subsequently trialled the model on the West Coast Main Line.
Challenges
The pace of AI adoption in organisations across the civil engineering sector has raised some concerns about skills and capacity. Some tools require specialists to bridge the gap between traditional civil engineering and advanced computing skills. While in-work learning can be used to upskill workers, there are concerns it might not be enough to bridge the gap.
Older assets also pose challenges for the extent to which a digital replica can be created. While some companies and organisations are teaming together to bridge data gaps, it will require more resources and time to run these digital twins effectively.
Conclusion
Digital twins are transformative technology for the rail industry, enabling smarter, more efficient and more sustainable operations. With applications across infrastructure monitoring, fleet management and safety, digital twins are becoming essential tools for the modernisation of rail systems worldwide.
Bibliography
- Integrated Network Management Digital Twin: Economic Benefits Analysis, UK Government Publications, September 2024
- AI Digital Twin Successfully trialled with HS1 and Network Rail, New Civil Engineer, October 2024
- DfT and Arup detail 850M potential benefits of integrating transport networks with digital twins, New Civil Engineer, November 2024
- Network Rail uses digital twins for the Transpennine Route Upgrade, Bentley, 2022
- The Digital Twin Revolution: Transforming Rail Infrastructure in the UK, On Trac Ltd, March 2024
- Great British Railways will increase data sharing for better twins…, New Civil Engineer, February 2024
- Digital Twin White Paper, Amey, May 2022
- Network Rail High Speed and HS1 collaborate to spearhead the digital future of UK rail, Aerogility, October 2024