Digital twin technology
A digital twin is a continuously updated virtual model of a physical asset or system that mirrors its real-world state in real time, enabling simulation, analysis, and decision support without intervention in 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 is used across a spectrum of technical maturity, from static 3D models used for design review to fully dynamic, sensor-fed systems that update in near-real-time. The meaningful distinction 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).
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.
Alstom has developed digital twin applications for fleet maintenance prediction. DB’s Digitale Schiene Deutschland (Digital Rail Germany) programme, in collaboration with Nvidia, is building a country-scale digital twin of the German rail network to simulate automatic train operation at network level.
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.
EU-Rail’s broader digitalisation programme includes digital twin development, particularly for ERTMS Level 3 deployment, where accurate real-time train positioning underpins moving-block operation.
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.