Digital twin’s dawn has seen the technology increasingly rolled out across a range of sectors in efforts for organisations and industries to evaluate and optimise processes before committing. The adage ‘measure twice cut once’ applies as the technology allows companies to incorporate testing of numerous strategies to see which is most effective before large-scale real-life implementation.
Yet as IoT continues its crusade, digital twins look set to play an important roll in the implementation of smart cities.
Dissecting digital twins
A digital twin is a digital representation of a physical object or system. The technology can be used to represent buildings, factories and even cities and processes. To do this, digital twins use a computer program that takes real-world data about a physical object or system and inputs and produces predictions or simulations of how that physical object or system will be affected by those inputs.
NASA first used the concept to simulate full-scale mockups of early space capsules on the ground to mirror and diagnose problems that could be experienced in orbit. Like many innovations utilised by the space industry, it eventually found its way to public enterprise, and now, it’s given way to fully digital simulations.
In 2017, Gartner pegged it as one of the top strategic technology trends, saying that “within three to five years, billions of things will be represented by digital twins, a dynamic software model of a physical thing or system”. A year later, Gartner once again named it a top trend, stating: “With an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future.”
Smart cities’ symbiotic relationship with digital twins
Smart cities are currently poised to be implemented in countries that have the necessary connectivity to enable such a phenomenon. Being a costly endeavour means that cities looking to implement this are already some of the wealthiest: Amsterdam, New York, Seoul, Singapore.
A 2019 Smart America study estimated cities will spend around $41 trillion in 20 years upgrading all the necessary infrastructure.
With such a big project like turning a city smart, costs can easily run over as sensors are placed and re-placed for maximum data grabbing effect. Yet, by building a data model of the city earlier and trailing it on a digital twin, then not only are costs saved in reposition, but outcomes are maximised as users will have been able to test more of the variables.
The city of Birmingham in the UK is currently securing £1 million in funding to trial a digital twin for exactly this purpose. In its efforts to reach net zero, the city will be set up to gather real time data from sensors to measure energy output, pollution, and traffic congestion. This real data will then be feed into the digital twin to run simulations, study performance issues, and generate possible improvements in how the council runs it services.
Equally, the Isle of White in the UK has teamed up with Fujitsu for the development of a new ‘digital rehearsal’ technology to better inform public policy and business planning. Fujitsu’s demonstration of the technology showed realistic simulations of the effects of traffic measures by reproducing people’s movements on a digital twin. This will feed into its cooperation with shared mobility company Beryl to improve the operation of shared e-scooter services on the Isle of Wight.
This piecemeal fashion of smart city implementation, through the aid of digital twins informing the strategy, could be how the building blocks of smart cities begin adding up.
Dilemmas with digital twins and smart cities
ABI Research has predicted that more than 500 cities worldwide would be using some kind of digital twin technology by 2025.
Yet, digital twins are an ecosystem in themselves. Therefore, developing and maintaining comprehensive digital twins that cover all aspects and developments of a smart city can be a big undertaking. Ensuring scalability, especially for large cities with extensive infrastructure and multiple domains, requires significant computational resources and advanced modelling techniques. Although costs have gone down, as the cities and its infrastructure expand, so will the data it will have to collect to maintain an accurate model.
The impetus for such undertaking is there. ABI estimated in its report that smart cities would presently lead to cost savings of $5 trillion.
And it shows. In Shanghai and Singapore, digital twin cities are already seen as essential for city planning and managing public services. In Brisbane, an ever-evolving digital twin is helping engineers to build the city’s first subway, and Rotterdam, Europe’s largest port, is developing a digital twin to run its operations in what’s being dubbed the ‘smartest port in the world’.
Whether or not digital twins will play a key role in the broader smart city revolution remains to be seen, but what is evident is their current utility and real role in helping to roll out everything from smart ports to net zero achieving cities.
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