Have you heard of digital twins? And we’re not talking about your online alter ego.
A digital twin is a virtual depiction of an object or system that uses real-time data to create an interactive simulation to assist decision-making – and has become the new craze in urban planning.
Originally a term from the realm of manufacturing and design, digital twins have evolved to play a crucial role in urban planning, offering a virtual mirror of our cities. Digital twins enable a comprehensive understanding of the complexities of a city, facilitating data-driven decision-making and optimizing resource management. Digital twins of cities use various forms of data, which provides the model with accurate imitations of the city’s landscape for future planning and decision-making.
Like a virtual mirror of their physical counterparts, digital twins give the opportunity to simulate real life – ranging from infrastructure, construction, traffic patterns, and even energy consumption and distribution.
According to a report from global tech firm ABI Research, they estimate that cities will save $280 billion by 2030 from using digital twins for more efficient urban planning. (Source)
How Digital Twins Are Used In Urban Planning
Digital twins of major cities such as Shanghai and New York are being made using various forms of data to stimulate the everyday patterns and activities of their urban structure.
Digital twins provide a computerized view of a city’s dynamics, allowing planners to simulate scenarios, identify potential issues, and devise effective solutions.
Role of Data in Digital Twins
Data is integral for digital twins, and creating a digital replica of a city involves acquiring vast amounts of information from various sources. This data can include geographical data, satellite imagery, sensor data, social media feeds, and more. The integration of this diverse dataset allows for a dynamic and real-time representation of the city.
The type of data that digital twins of cities use:
- Satellite data
- Sensor data
- Real-time data
- Artificial intelligence
- Geographical data
- Social media data
- Data points from autonomous cars, delivery drones, construction equipment
Geographical data, including topography and land-use patterns, provides the foundational layer for digital twins. Satellite imagery helps in visualizing the physical features of the city, while sensor data from various sources, such as traffic cameras and weather stations, contribute to understanding real-time conditions. Furthermore, social media feeds and other online platforms offer insights into the social aspects of the city, reflecting public behaviors.
How Digital Twins of Cities Are Made
Building a digital twin for a city involves a series of intricate steps, starting with data acquisition and integration. The collected data is then processed and analyzed to create a three-dimensional model of the city. Advanced technologies such as Geographic Information System (GIS) mapping, AI, and machine learning algorithms play a pivotal role in refining the accuracy of the digital twin.
How they are made:
- Infrastructure – buildings, roads, parks, etc
- Mobility – public transportation, cars, etc
- Digital layer – connections from satellites and sensors within the city
- Virtual layer – potential project implementation
Once the model is established, it becomes a dynamic entity, continuously fed with real-time data from various sources. This ongoing stream of information ensures that the digital twin remains a faithful representation of the city’s current state. The interactive nature of digital twins allows planners to simulate different scenarios, assess potential impacts, and make informed decisions.
Digital Twins and Smart Cities
The concept of smart cities revolves around leveraging technology to enhance the quality of life for residents, improve efficiency, and promote sustainability.
The frequent updates fueling digital twin developments create virtual, live, mirrors of their physical models. This ensures accurate information among infrastructure for planning and decision-making. For example, digital twins use data on accident near misses that are usually not reported so that the safety of an intersection can be more precisely measured.
Optimizing Infrastructure
By simulating the impact of new constructions or changes in traffic flow, cities can make informed decisions to enhance efficiency and reduce resource wastage.
Enhanced Resource Management
Smart cities prioritize resource efficiency, and digital twins contribute by offering real-time insights into energy consumption, water usage, and waste management. This information aids in developing sustainable strategies for resource allocation.
Transportation Planning
Digital twins help in simulating traffic patterns, optimizing public transportation routes, and facilitating the integration of smart mobility solutions, including autonomous vehicles and ride-sharing services.
Environmental Sustainability
Digital twins provide a platform to model and assess the consequences of development projects on the environment, enabling cities to make eco-friendly choices.
Resilience Planning
Digital twins assist in creating resilient urban plans by simulating disaster scenarios, helping cities prepare for emergencies and recover swiftly.
Conclusion
By harnessing the power of real-time data, advanced technologies, and predictive analytics, digital twins empower urban planners to make informed decisions, optimize resource utilization, and pave the way for the development of smart cities.
