Digital twins amplify the intelligence of big cities

Digital twin technology is helping cities improve the planning, building, monitoring, and sustainability processes of urban centers and thus accelerate the path towards smart cities. The practice develops copies of physical objects or procedures in a digital environment to simulate different scenarios by capturing data in real-time, often using IoT sensors to monitor real-world changes and predict potential impacts and solutions.

World-renowned for having some of the most advanced digital twin models for smart cities, the city-state of Singapore – there is a controversy as to whether Singapore should be considered a city or a country – has pioneered the development of a semantic 3D model, including buildings and infrastructure and land attributes, among other elements, to conduct virtual experiments based on the digital version of the city and simulate scenarios before implementing any new policies in the real world.

One of the pillars of digital twins is precisely Building Information Modelling (BIM), the discipline used by architects and builders to plan and design buildings, bridges, and other infrastructure. However, digital twins go beyond that, evolving over time to generate more value at each new stage of the asset lifecycle.

Experts point out that for digital twins for smart cities to generate value, one must rely not only on a static representation but also on insights that feed the system continuously. Maintaining accuracy is crucial. Every detail must be updated, whether with data from the environment or the infrastructures, otherwise, the digital twin will fail to represent reality accurately. Sensors embedded in the infrastructure and installed throughout the city will help with the updating tasks.

For example, in addition to static data, the Virtual Singapore platform incorporates dynamic real-time weather or traffic information. With up-to-date information always at hand, Virtual Singapore is able to simulate emergency situations in the city and establish the most appropriate evacuation protocols at the time.

In addition, Singapore can learn in detail about the current landscape to better design upgrades or redevelopment projects, for example by envisioning how the skyline would change if new buildings were approved. It is also possible to identify opportunities to expand green areas, increase recycling levels in the city, or optimize the use of underground spaces. Other actions enabled by Singapore’s digital twin models are:

• Analyse transport flows and pedestrian movement patterns;
• Identify areas of poor telephone network coverage and how to improve them;
• Assess buildings with the highest potential for solar energy production and therefore most suitable for the installation of solar panels;
• Reveal opportunities for the development of advanced 3D applications and tools.

Shanghai, the largest city in China and one of the most populous in the world, has also created its own virtual clone to monitor everything from traffic and construction to bridge maintenance, improve services, make forecasts and assess the consequences of population growth, climate change, natural disasters or pandemics.

An area of about 4,000 square kilometers was covered, and more than 20 landmark structures were modeled. An algorithm uses data from satellites, drones, and sensors to generate digital versions of other buildings, roads, canals, and green spaces and keep Shanghai’s digital twin continuously updated almost in real-time.

The simulation is divided into segments of two square kilometers. Nearby objects appear in greater detail, showing accurate environmental reflections, soft shadows on surfaces, and realistic shading effects according to the weather.

Beyond cities

The concept of digital twins is not new and dates back to the time NASA used mirror systems to help rescue the Apollo 13 space mission. However, in recent years, it has gained new momentum with its application in various industries to the point where one study predicts that creating digital replicas of real-world infrastructure will drive a $48.2 billion market by 2026. Recently, the demand for digital twins has been accentuated in the pharmaceutical and healthcare industries due to the pandemic of COVID-19.

The economic value of digital twins varies widely depending on the monetization models adopted. For example, in the service sector or complex and expensive industrial or business processes, reducing asset downtime and overall maintenance costs will be extremely valuable, making digital twin’s initiatives more attractive. “Digital twins will drive the business of the Internet of Things (IoT) by offering a powerful way to monitor and control assets and processes,” said Alfonso Velosa, research vice president at Gartner.

However, the executive warns that to truly extract value from digital twins, economic and business models need to be developed that consider the benefits in light of the development costs and ongoing maintenance requirements of digital twins. Developing and ensuring support for digital twins will require continuous updating of data collection and curation capabilities, as well as adaptive algorithms and analytics. “The complexity of digital twins will vary by use case, industry, and business objective,” adds the Gartner executive.