Digital twin technology in rail
A digital twin is a virtual model of a physical asset or system in European rail, fed by live and historical data so scenarios can be tested without disrupting physical operations.
In rail, digital twins are applied at two distinct levels. At the asset level, a digital twin represents an individual component or vehicle: a traction motor, a bogie, or an entire train. The twin is populated with the asset’s design specifications, operational history, and current sensor readings, allowing its condition and remaining useful life to be modelled continuously.
At the network or infrastructure level, a digital twin represents a section of track, a station, or an entire operating environment — enabling simulation of timetable scenarios, capacity constraints, and maintenance interventions before they are implemented in the physical system.
The term covers everything from static 3D models used in design review to sensor-fed systems that update continuously. What separates a twin from a conventional monitoring dashboard is simulation capability: a twin can model future states and test interventions; a dashboard visualises current or historical data.
How it works
A digital twin combines three elements: a data layer (sensor feeds, maintenance records, operational history), a model layer (physics-based or data-driven representations of how the asset behaves), and an application layer (the tools through which operators interact with the twin — dashboards, simulation environments, maintenance planners) — sitting within the wider digital systems and software stack that rail operators now depend on.
For rolling stock, onboard telematics provide the continuous data feed. For infrastructure, wayside sensors, inspection data, and geometry measurement feeds update the twin’s representation of track condition.
European deployment
Digital twin deployment in European rail is concentrated among the largest operators and in new infrastructure projects, where digital models can be specified from the outset. Retrofitting digital twin capability onto existing fleets and older infrastructure requires sensor installation, data normalisation, and model development — a multi-year investment.
Since 2022, Deutsche Bahn and Nvidia have been developing a country-scale digital twin of the German rail network as part of the Digitale Schiene Deutschland (Digital Rail for Germany) programme, intended to simulate automatic train operation across the network.
Europe’s Rail Joint Undertaking (EU-Rail)’s Flagship Project 3 (FP3-IAM4RAIL) includes a dedicated digital twin cluster within its wider predictive maintenance and asset monitoring work, running demonstrations across rolling stock, track, and civil infrastructure.
Challenges and constraints
The primary challenges are interoperability and data continuity. A digital twin’s analytical value depends on the completeness and consistency of its data feed; gaps in sensor coverage or data quality degrade model accuracy.
Across heterogeneous fleets — different train types, generations, and manufacturers — establishing a consistent data architecture is technically and contractually complex.