Authors: Hossein Naderi, MSc & Alireza Shojaei, Ph.D.
What is Digital Twin? And what components is it made of?
How can Digital Twins pave our way in effectively responding to a city’s needs?
Are Digital Twins being used effectively today? What should be done to advance them?
According to the world bank report, more than half of the world’s population lives in cities. This trend will continue by 2050 when nearly 70% of people live in cities. This trend of growing cities raises concerns about limited resources and their influence on the quality of living. Under such a circumstance, cities require more optimized systems and effective designs to provide all citizens better services. To this end, various technological advancements can be utilized to make more informed decisions in smart cities. Digital Twins have been introduced as promising solutions for accelerating the transition to smarter and more effective cities. But before looking into how Digital Twins can improve our future smart cities, we must look at its definition and how it works.
Digital Twin is defined as a virtual replica of a physical asset. This virtual replica should be connected to the physical entity in real-time to reflect one or multiple behaviors of the physical entity. A Digital Twin can also be connected to other Digital Twins in the lower, same, or upper-level systems, which can create a system of systems altogether. After defining Digital Twin, we need to discuss the main technologies or components of constructing Digital Twin to understand the overall function of Digital Twin. As a digital replica abstracting physical counterpart, Digital Twin development requires four components: (1) an information model abstracting information of physical entities, (2) a data acquisition mechanism that collects and bi-directionally transfers data between a Digital Twin and its physical counterpart, (3) a data processing module extracting hidden knowledge behind heterogeneous multi-source data and enabling insightful decisions (4) a synchronization mechanism between three mentioned components to guarantee a smooth data distribution between various modules and the physical entity.
We must look at how Digital Twin works to address the second question. Data is constantly, and in real-time, transmitted from a physical entity to its virtual replica (Digital Twin). In this stage, data is harnessed and processed in various modules to generate new information. This information, then, comes back to the physical entity to support more insightful decision-making or effective services. This brings many opportunities that can change how we manage cities and provide services. Existing cities contain a massive number of services that constantly interact with each other, creating a super complex system of systems. Overall, Digital Twins, fueled by data, can give us a deeper understanding of how this complex system works and serve as a test bed to see how our new ideas will eventually play out in such a system. Digital Twins treat data as raw material for analyzing and creating more effective services in smart cities.
Next, let’s look at ways that Digital Twins can be applied to build effective solutions at different levels within the context of smart cities. City-level Digital Twins can solve macro-level problems. For example, a city-level Digital Twin can harness city data to reduce traffic, minimize waste, and increase energy efficiency at the city level. In a more specific way, an infrastructure-level Digital Twin can improve services at the level of the infrastructure. For example, Digital Twins of self-driving vehicles can provide access for minorities in smart cities, leading to more diverse and inclusive communities in the future.
Furthermore, the Digital Twin idea can be implemented at a more microscopic level, such as the Digital Twins of humans. For example, the Digital Twins of citizens in smart cities can provide them with insightful information about their interactions with cities’ infrastructures. This can raise many opportunities to save time and perhaps more healthy societal behaviors. Moreover, Digital Twins, on different levels, can interact with each other to resemble the existing complex system of systems in cities and build more smart solutions. For example, vehicles’ Digital Twins can interact with roads’ Digital Twins in cities to report all problems, enabling predictive maintenance and considerable cost saving in city operations. This should not be restricted to same-level Digital Twins. For example, Digital Twins of citizens can be connected to upper-level Digital Twins, such as city-level Digital Twins, to improve user experience in smart city services.
Despite the opportunities discussed in the previous paragraph, we must look at the current state of Digital Twin development to build a realistic expectation of this technology. Except for a few real projects, most Digital Twins are developed to mirror a middle-level physical entity, such as buildings and vehicles. Although the current state of Digital Twin development faces different open issues, increasing advancements in twinning technologies and interoperability promises more advanced interaction with physical twins and more accurate Digital Twins soon. For example, Microsoft has created Azure Digital Twins as an open-source platform, a JASON-like language based on Digital Twin Definition Language (DTDL).
Even though advancements have been made in the context of Digital Twin development, interoperability remains one of the most unexplored issues. This means that existing Digital Twins need to be more capable of integrating with other Digital Twins to generate a comprehensive solution for smart cities. Different standards are currently under development that will provide solutions for this challenge in the near future. BuildingSMART is developing the IFC5 standard, moving from file-based information silos to a scalable solution. Open Geospatial Consortium is working on CityGML 3.0 as a new BIM-compatible solution allowing data to be encoded in more open schemas, such as JSON. Furthermore, Digital Twin Consortium proposed a framework for creating complex systems interoperable at scale.