Leveraging Digital Twins for the Rail Industry: Opportunities and Impact in Australia, Europe and the UK

Decision Analysis Services (DAS) is an independent professional services company, serving clients globally within the Energy, Defence, Government, Health, and Transport sectors. We specialise in bridging the gap between strategy and operational execution.

DAS contributes to research, via our sponsored PhD and MSc students, using Artificial Intelligence (AI) and Machine Learning (ML) tools to interrogate data and provide foresight to decision makers. This, combined with the work undertaken by our Innovation Lab, places us in the position of being able to capitalise on, and incorporate AI, ML and Data Science developments in work for our clients.

Introduction

Digital twins comprise of a virtual representation of a physical system, process or entity which is updated using real-time data. These virtual representations can take the form of physics-based models, simulations, statistical models, etc. which mirror the characteristics and behaviours of their real-world counter parts. Traditional digital twins, however, often rely heavily on physics-based models (models of real-world systems that follow the laws of physics), which can be limited by the availability of accurate data and the complexity of the system being modelled.

Our recent ‘Foresight: Expanding the Methods and Applications of Digital Twins’ article assesses the revolutionary potential of AI-enabled digital twin technology to the rail industry. With a growing demand for rail transport across Australia, Europe and the U.K., we explore how AI-enabled digital twins can address some of the critical challenges the industry faces and provide actionable insights that drive efficiency, safety, and sustainability.

Background

The rail industry faces complex challenges related to efficiency, sustainability, and performance. In the UK, aging infrastructure and operational inefficiencies strain the system, while in Australia, vast rail networks in remote environments make asset monitoring and maintenance complex. Freight logistics, particularly in mining exports, also face inefficiencies, increasing costs and delays. Both regions must address sustainability goals, reducing emissions and improving maintenance strategies.

AI-enabled digital twins offer a transformative solution to these challenges through real-time monitoring, predictive analytics, and simulation capabilities.

Leveraging AI-enabled digital twins in rail

The integration of digital twins and AI technologies offer numerous opportunities for the rail industry. These digital replicas of rail assets and networks integrate Internet of Things (IoT) sensor data, Machine Learning models, and System Dynamics simulations to deliver actionable insights.

IoT devices can gather real-time data on infrastructure, rolling stock, and environmental conditions, which can be combined with historical data, GIS information, and operational logs for a comprehensive whole system overview. Machine Learning predictive models can anticipate future maintenance requirements, optimise schedules, and enhance asset performance. Additionally, System Dynamics models can simulate complex interactions within the rail network, such as passenger flows, freight logistics, and energy consumption, facilitating complex scenario planning.

This advanced functionality can predict equipment failures and schedule repairs proactively, optimise service operations to reduce fuel usage and emissions, and simulate the impacts of extreme weather to develop mitigation strategies ahead of time, minimising unplanned downtime and service disruptions. Furthermore, AI-enabled digital twin technology can optimise freight and passenger operations by modelling and refining schedules, routes, and asset utilisation. It could also enhance sustainability efforts by simulating energy efficiency measures and environmental impact, ensuring rail networks operate with minimal emissions and resource waste.

By integrating AI and real-time data, digital twins can provide rail operators with foresight, enabling smarter decision-making that enhances efficiency, safety, and sustainability across the rail lifecycle.

The impact across rail domains

In asset management, the technology enables real-time monitoring of rail networks, which is particularly valuable for Australia’s vast and remote rail systems. In the UK, where aging infrastructure requires frequent maintenance, digital twins help optimise schedules and extend asset lifespans, reducing disruptions.

For freight and passenger operations, digital twins can enhance efficiency by simulating load distribution and route planning, which is crucial for Australia’s mining and agricultural exports. In the UK, optimising train schedules and passenger flows helps reduce congestion in urban networks and improve service reliability.

When it comes to construction and infrastructure projects, digital twins allow planners to assess environmental and economic impacts before breaking ground. This is particularly useful for Australia’s efforts to expand rail connections in remote regions and for minimising disruptions to urban projects such as HS2 in the UK.

Beyond operations and infrastructure, digital twins also support training and workforce development. By integrating augmented and virtual reality (AR/VR), they create realistic simulations for training operators and maintenance crews. These tools prepare staff for complex scenarios, from extreme weather conditions to system failures, improving response times and overall safety.

Finally, in pursuit of sustainability goals, AI-enabled digital twins provide insights into reducing energy consumption and minimising environmental impact. They support the transition to renewable energy sources for rail electrification, helping both Australia and the UK align with long-term sustainability targets.

Conclusion

AI-enabled digital twins have the potential to transform the rail industry across Australia, Europe and the UK by improving asset management, streamlining operations, and supporting sustainability goals. By creating a real-time digital replica of rail infrastructure, rolling stock, and networks, digital twins help operators make smarter, data-driven decisions.

These technologies allow for predictive maintenance, reducing costly breakdowns and service disruptions. They optimise freight and passenger scheduling, making transport more efficient and reliable. They also support sustainability efforts by identifying ways to reduce energy consumption and emissions.

As the rail industry continues to modernise, digital twins will play a key role in ensuring networks are safer, more efficient, and better prepared for future challenges. By embracing this innovation, rail operators and governments can improve service reliability, reduce costs, and build a more sustainable transport network.