Digital twins and the future of railways

5 minute read

28 October 2021

By Paul Corcoran

Contributor, Upstart

Paul Corcoran, Managing Director, Interimconsult, looks at how digital twins can help transport networks, including the railways, bounce back after the Coronavirus lockdown.


The past few years have been tough for the railways and it’s going to get tougher. The national and global economic impact of Covid-19 has disrupted passenger travel patterns with the result that the industry cost-revenue gap is larger than it has ever been and will remain so for some time. 

Meanwhile, resources and budget are needed for once-in-a-lifetime upgrades including electrification of tracks and trains, and the switch to digital signalling. Massive ‘cathedral’ style projects, such as HS2, are under greater scrutiny than ever before, while the pressure to improve the overall customer experience is mounting. 

For anyone responsible for public transport strategy, construction and management, this is probably the most disruptive era of the past few decades. 

There is no magic bullet for any of these challenges. But digital twins have the potential to reduce complexity and costs across all of them. 

Put simply, digital twins are simulations of an object or system across the entire lifecycle from research and development, to lifetime maintenance and renewal. They can be used to model competing hypotheses and outcomes, and help reduce delays and costs associated with infrastructure projects. 

So what?

We believe that the use of digital twins in the railways is at a tipping point now that the affordability of projects is under scrutiny and there is enormous pressure to attract passengers. Opportunities include: 

  • Electrification and digital signalling: A good example of where the creation and maintenance of digital twins is extremely useful. Not just configuring and testing systems before putting them on the ground to limit on-site work, but in capturing the location of existing infrastructure assets and connections such as power distribution points, third rails and contact shoes on the train. 
  • Proactive maintenance: Based on near and real-time data across the entire railway infrastructure, digital twins make it possible to predict and plan interventions for the maintenance and repair of individual components. Using a twin for off-site simulation and testing can help prevent failures, increase network availability and provide customers with reliable, punctual services.
  • Open supply chains: Digital twins can be shared all the way down the supply chain so there is ‘one model of the truth’. This means that different suppliers can collaborate when using the twin for modelling operations.
  • Transforming the customer experience: Live data and forecasts from a digital twin make it possible to combine data models for seating reservations so that passengers can choose a seat from their phone even as they are waiting for the train to pull in to the platform. Live passenger flow monitoring in stations can be used to automatically allocate entrance and exit gates according to footfall entering or leaving the concourse. 
  • Supporting the drive to net-zero: Reducing carbon emissions during deployment, configuration and testing through simulation. Also, optimising and accelerating the replacement of diesel trains by modelling their operation and energy use on a digital twin of the railway network. 

So what’s next?

Railways are complex systems and digital twins can provide valuable insights when looking at network enhancements, renewals and maintenance.

But the industry urgently needs a data model of its built assets so it can simulate whole-life operations, costs and carbon emissions. This also needs to take into account the interaction of trains, infrastructure and technology on asset-intensive railway networks. 

One of the biggest challenges here is data integrity. This is exacerbated in railway projects that inherit ageing legacy infrastructures and components. In many cases, the data points reside in complex paper trails. In some cases, through many years of repairs and partial renewal, they may not even exist at all.

At the other end of the scale, how do you filter out the digital noise from more recent deployments? Do you need to include data from every sensor and audit the configuration of every hard drive? If not, how do you choose what to capture and what to ignore?

This is where a digital twin expert can help. In many cases, they can bring transferable knowledge from construction, manufacturing and other sectors that have undergone similar disruption.

Such expertise should also inform:

  • The minimum viable data to build a digital twin: Can you survive with digital records, sensors or software embedded in more recent components? Or do you need to dig deeper into pre-digital records? 
  • Data cleansing and pre-processing: What tools and expertise are needed to validate, pre-process and format data from multiple sources so that it can be imported into the digital twin model?
  • Priorities and focus: Where will you apply your models? An expert will be able to show you how digital twins can be implemented at different stages including design, build, maintenance and operation. 
  • Opening up the model: Another way of importing knowledge is to invite other organisations to observe and model data based on success in other projects. This might include retail (customer loyalty) and automobiles (embedded sensors in vehicles for maintenance). 

In spite of recent disruption to passenger numbers, we believe that there are huge opportunities to grow passenger revenue after the pandemic. Digital twins have a central role to play in this evolution by increasing value for money, reducing carbon and transforming asset management. At the same time, they can help deliver more punctual services, at optimal frequency, and improve the passenger experience. 

To find out how your organisation can create and deploy a digital twin, get in touch with Upstart TODAY.

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