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Digital Twins are used increasingly to support urban planning processes – by visualizing urban data, show-casing future scenarios and many other use-cases. In general Digital twins are virtual representations of an object, process or system that can be used to run simulations to optimise efficiency and examine what-if scenarios. The technology has been primarily used for manufacturing to test products (e.g. as of 2018, GE had 1.2 million digital twins for 300,000 types of assets) but is quickly expanding to buildings, supply chains and entire cities as digital planning technology advances (Castro, 2019). Integrating data from the internet of things (IoT) with the advanced modelling capabilities of technologies such as geospatial information systems (GIS), virtual and augmented reality (VR/AR) and building information modelling (BIM) allows governments and industry to create predictions of how systems will react and respond to real-world data. Creating a feedback loop between the virtual and real worlds results in substantial improvements of processes and impacts, with time-saving and financial benefits.

The concept of digital twins is not new; for example, NASA has been running simulations of spacecraft for decades, but the rapid growth of connected sensors and endpoints with the rise of the IoT and advancements in artificial intelligence has opened up a myriad of possibilities for the planning and analysis tool. Potential uses for digital twins are still being imagined. Uses for cities currently include using digital twins to plan transportation systems, prepare for natural disasters and identify optimal locations to install solar panels. Future uses could include predicting how a disease will spread and informing optimal lockdowns and hospital reservations or using the tool to facilitate collaborations with other cities that have shared problems and mutual goals.


The main goal of Digital Twins is to ease urban planning processes, use data and increase transparency. Besides that, the solution achieves the benefits listed below. Whereas some benefits are likely to be fulfiled with a basic implementation of the solution, the fulfilment of the potential benefits depends on the functions implemented in a specific project.

Main Benefits
  • Enhanced data analysis

  • Facilitating citizen engagement

  • Improving personnel efficiency

  • Improving life quality

  • Enhanced data collection

Potential Benefits
  • Efficient integration of renewables

  • Increased data transparency

  • Enhanced data collection


Functions help you to understand what the products can do for you and which ones will help you achieve your goals.
Each Solution has at least one mandatory function, which is needed to achieve the basic purpose of the Solution, and several additional functions, which are features that can be added to provide additional benefits.

Mandatory Functions
    Connect with data about the city

    accessing data (e.g. from sensors) about the different components of a city

    Simulate processes

    use real time data to create simulations that can predict how a product or process will perform

    Provide information to optimise processes

    provide decision makers with information on how to optimise simulated products or processes

Potential Functions
    Prepare for emergency situations

    Simulate disaster response to best prepare

    Communicate to stakeholders

    features enabling citizen participation and stakeholder collaboration


Digital twins are virtual counterparts to entities of varying complexity, with twins of entire systems likely being the most use to cities.


In the conventional sense product digital twins are  virtual counterparts of a real physical product, allowing engineers to explore and optimise the lifecycle of product designs. In the case of city this could be the simulation e.g. of public furniture such as benches etc


A digital twin of the entire manufacturing process. This model uses the virtual and physical worlds simultaneously to optimise the process with the aid of artificial intelligence and sometimes immersive holograms. In a city context this could include working with companies to have a twin for the production of solar panels or wind turbines.


The most advanced variant and most relevant to smart cities, system digital twins can capture real-time data from entire systems (including cities) to improve decision making and model how those decisions will impact the entire system. For example, this could include modelling how a new speed limit will impact traffic and pollution in a neighbourhood.

City Context

Digital twins are supported by cities with:

  • IoT sensors embedded in city’s core services
  • High levels of connectivity and useable data
  • GIS system in place

Supporting Factors

Digital Twins of smart cities is a new application of a number of technologies that need to be interoperable and high-performance. A capable and innovative team, with in depth knowledge of how these technologies and their data function, will support a city in getting the most out of their digital twin. A brief overview of some of these technologies that are integrated into digital twins follows:

  • Geospatial Information Systems: The core spatial modelling technology of a city’s digital twin, GIS connects different kinds of geospatial data to create a single view and provide advanced analytics of the system.
  • Building Information Modelling: When integrated with GIS, BIM provides a rich dataset for the built environment to create more accurate models. Real-time data requires the interoperability with the Internet of Things.
  • Augmented or Virtual Reality: Enhances real life perception of digital twins. This can be particularly useful in collaborative and participatory process in urban planning.

Stakeholder Mapping

Stakeholder Map of an Urban Digital Twin (BABLE, 2021)

Market Potential

Across all industries, the global digital twin market size was valued at USD 3.1 billion in 2020 with an expected CAGR of 58% until 2026, when it projected to reach USD 48.2 billion. The response to the COVID-19 pandemic is expected to be a major driver of the digital twin market growth, in particular in the healthcare and pharmaceutical industries, as well as for cities to better monitor outbreaks and respond to the changes in daily life brought by the pandemic (Markets&Markets, 2020).  

Cost Structure

Digital Twins on the city level are still a nascent technology and the cost structures will vary widely based on how advanced the software and hardware the city decides to invest in. One example, Virtual Singapore, had a budget of $73 million for developing their digital twin platform and for researching the tools and technologies required (NRF, 2020).

The creation of this Solution has been supported by EU funding

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Microgrids are emerging as an attractive, viable solution for cities, utilities, and firms to meet the energy needs of communities by leveraging more sustainable resources, while increasing resilience, reducing emissions, and achieving broader policy or corporate goals.

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Improving energy efficiency of the building stock in a city needs strategic and long-term thinking. Complex ownership structures, market barriers, diversity of building typologies, consumer preferences and multiple stakeholders involved makes energy efficient retrofitting a big challenge.

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On-line sales have become an essential part of retail business in recent years. Consequently, the volume of traffic caused by delivery services has increased rapidly. What impacts cities most is he final track of the supply chain, the so called “Last Mile” delivery.

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The city infrastructure must be able to respond to various challenges including catastrophic events, natural disasters, terrors attacks and further cases of emergencies. For that purpose, an integrated emergency handling system is required which closes the gap between emergency centres and citizens.

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