Digital twins have become one of the most talked about topics because of their promise to leverage innovation to improve design, visually enhance collaboration, and increase asset reliability and performance. However, rail is a very traditional and safety-sensitive industry, and with the backdrop of owner-operators and project delivery firms needing to work within tighter budgets, shorter deadlines, and with increased legislation, change can be slow and challenging.
While the risks associated with changing a tried-and-true formula weigh heavily on the minds of those responsible, the upside is that the highly complex nature of rail networks and systems allow for the opportunity to innovate and leverage technology to change the way rail networks do business.
Many owner-operators around the world have recognized the potential for digital twins in their work and have begun to explore the opportunities for applying big data analytics, artificial intelligence (AI), and machine learning (ML) throughout the design, construction, operation, and maintenance of rail and transit networks.
What Is a Digital Twin?
A digital twin is a digital representation of a physical asset, process, or system, as well as the engineering information that allows us to understand and model its performance. Plainly stated, a digital twin is a highly detailed digital model that is the counterpart (or twin) of a physical asset. That asset might be anything from a ticket machine or escalator in a station, through track and the switches and crossings within it, to related infrastructure like overpasses or overhead line structures, right up to and including an entire city.