Urban planning, or city design, has been around as long as cities have been in existence. When city populations expand, planning becomes a necessity to address critical needs such as sanitation, waste, water, transportation, communication, safety, health and more. And, much like the design of products has shifted from paper diagrams to computer-based models, city planning has also gone digital. The earliest iterations of these models tended to have a singular focus, such as a 3D model or virtual prototype of one product or one city road. However, technology advances in cloud platforms, the internet of things (IoT), artificial intelligence and machine learning have enabled more complex system-based models to evolve such as digital manufacturing, supply chain 4.0, advanced predictive maintenance and smart cities.i
When one of these models also includes data on how the process (or thing) works and develops over time, it is a digital twin. For example, a digital twin of a smart building would include real-time environmental and operational data on energy use, temperature, humidity, occupancy, security incidents, maintenance needs, external weather patterns and more. This enables the building to self-adjust to changes over time for optimal performance. When digital twins are linked together in systems of systems, they can be even more powerful. For instance, a digital twin of a smart thermal grid can facilitate the planning of heat exchange between buildings, making entire communities more energy efficient. And a digital twin of a city can provide a macro view of city interactions for what-if analyses before large capital investments such as major roadworks are made.
Digital twins in smart cities
Cities are essentially built environments in the physical natural world. As such, the what-if analyses of urban planning and management must take into account variables like climate and weather patterns, the availability of materials and the location of resources and more. Materials and architectures that work well in one climate may not work in others. Moreover, cities located in areas prone to natural disasters must take additional precautions for mitigation and response such as ensuring that buildings can withstand earthquakes.
Citizens represent a host of other variables that urban planning models must take into account such as demographics, resource consumption, transportation, land use, safety, waste and health. All of these variables represent data points that can be harnessed in digital twins to help urban planners, leaders and citizens make better decisions and solve problems. Updated by real-time data fed into them by sensor networks, digital twins provide deeper insights to improve resource management and quality of life. Here are a few examples of how this can work:
- City officials in Palo Alto, California discovered that one cause for traffic congestion was that people were driving around blocks looking for empty parking spaces. They deployed a digital twin of city parking spaces that monitor occupancy through a low-cost sensor network. That information is sent to the cloud where citizens can check parking availability through a website or app. Coupled with additional sensors that monitor auto and pedestrian traffic, it is helping the city determine where crosswalks should be placed and how traffic lights should be timed. In a similar fashion, Santa Clara County, California streams data from cameras and sensors on its streets to its Traffic Management System to calibrate traffic signal times for 1.5 million cars on a daily basis.ii
- Singapore launched Virtual Singapore, a digital twin and collaborative data platform intended to enable users from different sectors to develop and test new tools, applications and services, improve planning and decision-making and research ways to solve city challenges. Data sources include 3D city models down to the building materials and terrain attributes, city data, real-time operating data from sensor networks and more. It enables users and city leaders to run simulations of everything from population growth to public events to natural disasters to determine the best response. Infrastructure changes, such as building a new stadium, can also be modeled to test how it might impact traffic, pollution, population density and more.iii
Virtual Singapore (Source: Engineering & Technology)
Connecting the digital dots
Digital twins depend on real-time data updates from sensors located throughout the city. That requires a distributed IT architecture that can collect and analyze data in real-time at the edge while aggregating data needed to improve the core models based in the cloud. A previous blog in this series on how to build a smart city outlines how to implement a distributed IT architecture like this leveraging Interconnection Oriented Architecture™ (IOA™) best practices.
Data-enriched digital twins can help cities design better urban environments and operate them more efficiently, improve city services and citizen experiences. And, by utilizing open data frameworks as part of the digital twins, planning bodies can enlist the “wisdom of the crowd” to involve constituents in the decision-making process. Citizens can see in real-time the impact that different “what if” scenarios would have on their community and help determine the best option.
As smart city digital twins evolve, IT providers will need to have methods in place for dealing with ever-expanding data volumes at the edge as well as a myriad of hybrid multicloud smart city applications. IOA best practices offer a proven roadmap to success by optimizing networks, simplifying hybrid multicloud complexity and distributing security controls, and data and analytics to the edge. To learn more, download the IOA Playbook for architecting for the Digital Edge.
Watch the Interconnections blog for future articles on smart city perspectives around the world. Don’t want to wait? Check out the whole smart city series below (more to come!).
Part 4: Digital Twins – The Secrets of Digital Twins for the Cities of Tomorrow (see above)
[i] McKinsey, Digital manufacturing: The revolution will be virtualized, Aug 2015;Digitally enabled reliability: Beyond predictive maintenance, Oct 2018; Supply Chain 4.0 – the next-generation digital supply chain, Oct 2016; Smarter cities are resilient cities, Jan 2019.
[ii] Challenge Advisory, The Smart City Concept Through Digital Twins, Oct 2018;ZDNet, Palo Alto CIO: What are smart cities?, Jan 2016; Government Technology, Santa Clara County Redefines ‘Street Smart’, May 2019.