Page 19 - Redefining smart city platforms: Setting the stage for Minimal Interoperability Mechanisms - A U4SSC deliverable on city platforms

In short, a local digital twin cannot be considered to be a complete digital representation of a city or
locality, as this would be impossibly complex. Rather, it is a digital representation that incorporates
data about those specific features of the local area that are needed to solve specific problems to
the level of completeness and accuracy required.

It is vital that the digital twins developed for different purposes can, over time, be added to or linked
together to enable more and more problems to be solved. Consequently, the architecture needs
to be designed so as to enable more and more data sets of different types to be incorporated, as
and when needed.

The architecture needs to allow data about the physical assets and infrastructure in the city to be
linked with data about the people in the city and how they live their lives. It also needs to allow
linking at various levels of scale, so that it can provide a city-wide view, a neighbourhood-wide view,
right down to an individual building, structure or piece-of- equipment view. It also needs to have
the capability to link to even larger-scale models to show how the city fits within its local region,
its nation, and potentially globally, so that it can take into account the flows of goods and people
in and out of the city and set the city within its wider geopolitical context.

A city digital twin is not only an information model, it is, more concretely speaking, cloud-native
software that represents the physical city across its life cycle, using real-time data to enable
understanding, learning, and reasoning. This pairing of the digital and physical worlds allows
analysis of data and monitoring of systems to head off problems before they even occur, prevent
downtime, develop new opportunities, and even plan for the future by using simulations. With a
digital twin of the city, the complexity and uncertainty of urban planning, design, construction,
management and service can be managed through simulation, monitoring, diagnosis, prediction
and control in the digital city.

However, there are still many challenges in implementing a digital twin-based smart city or
community. The main problem is the nature of vertical city management. This means that data about
different areas of city life are collected separately in different and often incompatible formats by
different city agencies, which are often already developing their own digital twins using proprietary
software and data models. This undermines any attempt to use digital twins to manage the city
as a whole. As a consequence, a local territory would have a number of digital twins relating to
it, including some that are closely modelling the actual physical dimensions and the geography,
whereas others would be more semantic in nature and less precise physically. They bring different
values, and most of the value comes from the semantic digital twins.

Imagine that a major fire breaks out in the city. To deal with it, it is vital to know where exactly the
fire is, how extensive it is and how fast it is spreading, where people are in the area and what are
their escape routes, how to deal with hazards such as gas pipes or dangerous chemicals, what is
the best route for fire engines (given current congestion on the road network), what likely threats
there might be to human life, and so on. It is also important to provide all the different agencies that
are dealing with the fire with real-time information so that they can adjust their activity accordingly.

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