Digital Twins offer cities an affordable, low-risk chance to experiment, tweak, and reformulate potential plans and possible solutions in an easily understandable, life-like virtual environment.
Imagine if urban planners could test new concepts without risk and at minimum cost. What if we could see in advance what would go wrong, recalibrate based on this feedback, and test it again – all before breaking ground or disrupting everyday life?
Well, we can. That’s the point of Digital Twins.
Digital Twins in Simple Language
Anyone who has played The Sims or used a flight simulator knows that virtual replicas of the real world can be surprisingly lifelike. We can harness that kind of technology to understand and improve the cities we live in and the processes at play.
Digital Twins (DT) are complete virtual representations of real-world environments or products. They are used in all manner of professions and can replicate assets at any scale – from a basic machine up to an entire city.
It all starts with data. By inputting information, we tell the DT how the real version operates and how external forces interact with it. The bigger dataset we have available, and the more reliable the source is, the better representation the DT will be.
That data can be static, from or up to a particular point in time, or more dynamic, obtained by sensors and cameras monitoring the physical environment in real time.
With these foundations in place, we can apply a change to the DT and see how the environment alters, all without disrupting the functions of the real version.
We can apply as many changes to as many variables as the original data allows and use the output to steer real-world decisions.
As a visual representation, the DT can be easily understood by most people. Some cases even use three-dimensional modelling, allowing us to explore the environment like a VR walkthrough.
A word of warning: don’t confuse this with a simulation!
Simulations replicate a single variable, whereas Digital Twins can handle multiple dynamics.
Uses for Digital Twins
Digital Twins enable users to:
- Run and test plans and theoretical scenarios
- Assess and compare solutions to real-world challenges
- Visualise how an asset may react when variables are altered
- Identify weaknesses and advantages in existing infrastructure & all conceivable calibrations
- Make predictions about timelines
- Understand how cities may strain and flourish as they develop
As a visual tool, any evidence DTs offer is presented in a way that stakeholders find it easy to understand. This makes Digital Twins a powerful tool for urban planners, city leaders, and decision-makers when called upon to justify making changes to the city and spending public funds to do so.
DTs have been used in various cities around the world for many purposes. They are particularly useful for:
Planning PEDs
Positive Energy Districts are a network of interconnected buildings or neighbourhoods that decentralise energy production. Via local solar, wind, and thermal power generation, they produce enough renewable energy to meet the heating, cooling, and electricity needs of local inhabitants and contribute to the shift to a carbon-neutral energy sector. They monitoring how the mix of renewables in the grid, energy used, and potential improvements may accelerate the transition from fossil fuels to clean, green energy.
City Planning
A DT can be used to map an area then apply scenarios in order to read and optimise traffic flow. It may be predicting how current infrastructure will cope with changing mobility behaviours over time, or testing what impact a new road layout would have, simultaneously assessing the relationships between traffic volume, access from radial roads, adding cycle paths, narrowing vehicle lanes, painting extra pedestrian crossings, installing responsive traffic lights, the impact of additional street furniture, and new traffic calming infrastructure.
Flood Management
Urban sprawl undeniably changes the landscape, and as we seal natural surfaces with concrete and asphalt, stormwater has fewer escape routes. At least, this is what we assume. A Digital Twin can tell us for sure what affect building – or indeed rewilding – in a flood basic may do, and inform decisions on whether to proceed, and how to position water retention and flood defenses infrastructure to minimise future damage and disruption. Many insurance companies employ DTs to understand where the risks are greater, reflected in premiums.
Other examples include:
- seeing how retrofitting would affect the energy efficiency of a building
- identifying architectural design flaws before plans are approved
- prove to energy communities how to connect local infrastructure to maximise advantage
- understanding maintenance requirements and scheduling for a public park
- streamlining manufacturing processes while maintaining utmost safety measures
Types of Digital Twin
There are different types of Digital Twin. The one we choose depends on our needs.
1. Static Twins
The most basic. Built from real world data like any Digital Twin, except captured from a specific point in time. Doesn’t account for ongoing changes to the environment so the outputs may be less reliable than other types as the data becomes more out of date.
2. Dynamic Twins
When the virtual replica represents the physical version in real time. Multiple data sources from sensors, devices, and even satellites feed the model, keeping it up to date. This combination renders the “duplicate” reliable, accounting for interactions with external forces and changes to a location over time.
3. Operational Twins
The most robust, and complicated, version of a Digital Twin, which combines so much information it may require AI or machine learning to interpret it.
Whereas the two previous forms monitor changes in the physical environment (static) over time (dynamic), an operational twin gathers data from individual pieces of equipment (collectively called “the Internet of Things”) to assess how each contributing process is performing.
By way of example, we can see how all three models work in the same conditions – the heating system of a building:
- Static twin – used to understand the heating system’s performance
- Dynamic twin – allows us to monitor how and when the system is used by residents and see how conditions alter as we adjust variables such as turning up thermostats, opening windows, changing outside temperatures, etc.
- Operational twin – measures operational performance of boilers, radiators, and smart metres, the energy efficiency, and unit costs over time.
The more complex a Digit Twin is, the higher the running costs, data requirements, and expertise needed to interpret it, but also the more accurate the output will be.
Pros and Cons
As with any methodology, there are pros and cons to Digital Twins.
It’s important that decision-makers be aware that these can influence our use of DTs and weigh up any shortcomings against the advantages they offer.
Pros
- Leaves nothing to chance! Success of physical projects usually falls to an extent on trial and error – and what works with the conditions of one city, may not work in another, so even duplication of success is no guarantee. DTs remove much of the uncertainty
- The triangulation of multiple data sources of DTs makes them one of the most realistic, responsive, and reliable tools available to city planners
- Entirely operated in the virtual realm, DTs allow cities to continue testing and evaluating multiple possibilities until they are ready to implement real-world interventions, while keeping expenses, time, and potential risks contained
- By tweaking variables, we can assess endless possibilities at any scale and optimise solutions for whatever problem we set out to address
- Ongoing data monitoring using the virtual environment builds on existing knowledge, making the Digital Twin increasingly more accurate and intelligent the lomnger it runs.
Cons
- Data management professionals are used to the term GIGO – garbage in, garbage out: reliable output from the Digital Twin fully depends on the quality of source data
- Regardless of the quality of in- and output, its value still relies on interpretation, meaning there is plenty of room for human error (or AI bias)
- Increasing the number of data sources makes the digital replica more accurate – but also more expensive to implement & operate
- Citizens’ rights to data privacy can be put in jeopardy if data collection is too granular, e.g. tracking mobility patterns via personal smartphones. In these cases, cities should consider data masking – an irreversible form of anonymisation.
Digital Twins in Action: A CityChanger’s Story
CityChangers has shared a handful of stories featuring DTs. Maybe the most notable is that of architect and engineer Carlo Ratti, whose work is heavily influenced by data-driven insights into the world around us.
Traffic Efficiency
In his HubCab project with MIT, Carlo used data from sensors attached to taxis in over 170 million taxi trips in Manhattan, New York City, to measure pollution and evaluate the efficiency of car sharing.
With a virtual representation of the borough, he is able to map the routes these vehicles should take to maximise passenger pickups in the fewest trips possible, improving traffic flow and journey speed, while minimising car-sharing services’ contributions to bottlenecks and greenhouse gas emissions.
Equitable City Design
But maybe more impressive still is Carlo’s Favelas 4D project, where pedestrians walked around wearing laser scanners to map the sprawling, complex, and intricate informal structures of Brazil’s largest favela, Rocinha. Residents here have few legal rights or access to basic services like electricity and running water because the built environment wasn’t planned by the municipality but constructed home by home by some of the country’s poorest families, meaning there’s no official infrastructure. This is also undesirable land, prone to natural disasters like flooding.
Forming a static Digital Twin made of data strings rather than images, it does not infringe on residents’ privacy. A visualisation made of pure data like this can be used to:
- understand how policy and physical infrastructure can be maneuvered to help improve living conditions in informal settlements
- prepare places to be more resilient to natural disasters
- formally assign land to people occupying it
- inform decisions to provide access to public services like water and waste collection
The application of this DT will, according to Carlo, benefit policymakers and citizens by making them an integral part of the urban feedback loop. Put another way, the Digital Twin is giving many thousands of unheard citizens a voice.