Digital twins for urban planning: How does it work?

Digital twins are virtual replicas of physical entities, enabling real-time simulation and analysis. These 3D models are used heavily for urban planning to visualize current conditions and predict future developments, ultimately enhancing decision-making processes for more sustainable and efficient city management. They also help monitor infrastructure health, environmental conditions, and public transportation facilities to create urban projects that offer the highest efficiency and sustainability outcomes. These sophisticated models integrate data from various sources, such as geographic information systems (GIS), sensors, and demographic databases, to create a comprehensive and dynamic representation of a city's physical environment.

A notable example is Singapore's Virtual Singapore project, which uses real-time data to create an interactive digital twin of the city. This platform allows urban planners to simulate scenarios like traffic flow and environmental impact, testing potential solutions before implementation. By modeling infrastructure projects within the digital twin, planners can optimize designs and mitigate issues like traffic congestion, leading to smarter, more resilient urban environments.

Enhancing real-time monitoring and decision making

Continuous Monitoring

By integrating data from sensors, drones, mobile devices, and GPS devices, digital twins provide an up-to-date view of the city's infrastructure and environment, allowing quick identification and response to issues. Continuous monitoring through digital twins enables real-time oversight of urban systems like transportation, utilities, and public services. For instance, digital twins can monitor traffic flow and public transit schedules in transportation, making immediate adjustments to alleviate congestion. Sensors can also track other city utilities, such as water supply, by estimating water flow in pipes.

A digital twin can monitor other essential factors, such as weather conditions, air quality, and crowds, such as the number of people at a subway station or in a particular area. This real-time data enhances issue identification, emergency response, and maintenance operations, leading to a more resilient urban environment.

The New York Metropolitan Transportation Authority (MTA) utilizes digital twin technology and location intelligence to revolutionize its subway asset management and maintenance operations. The MTA aims to create a 3D map of its assets to help laborers view underground infrastructure conditions through their mobile devices or augmented reality screens. This visualization will help workers view and assess the health of tunnels, tracks, signals, and other utility infrastructure that need maintenance in real-time through 'x-ray vision' while looking at their devices from a pavement. 

Another notable project, DUET, in the Flanders region of Belgium, has developed a digital twin to calculate traffic volumes when certain roads are closed. The model also collects air quality data, which, viewed in conjunction with traffic data, allows for simulating the impact of traffic measures on the surrounding neighborhood.

Informed Decision Making

Digital twins empower urban planners to make better decisions by providing a dynamic, holistic view of city systems. By integrating real-time data from sensors and historical records, digital twins offer an up-to-date representation of urban environments. These data-rich models allow planners to monitor infrastructure performance, test projects before implementation, and collaboratively develop urban land. PwC defines these models as 'strategy accelerators' as they help public sector organizations gain better insights and create better solutions for cities. For instance, government officials can use a digital twin's data to assess a location's disaster preparedness by running simulations that can help decide ideal evacuation plans in emergencies. 

Bologna, an Italian city, is using digital twins to improve mobility, optimize energy usage, and mitigate climate change. The data collected by the digital twin from sensors has helped the city develop better cycling and tram infrastructure.

Predictive analysis and risk management

Predictive Maintenance

Digital twins use real-time data and advanced simulations to forecast infrastructure wear and tear, allowing for proactive maintenance. They continuously collect and analyze data from sensors embedded in infrastructure components such as bridges, roads, and utility systems. This data includes stress levels, vibration patterns, temperature fluctuations, and other relevant metrics that indicate the health and performance of these assets. 

By applying machine learning algorithms and predictive analytics, digital twins can identify patterns and trends that suggest potential failures or deterioration and alert engineers. AI-driven digital twins also effectively predict how structures withstand weather conditions, traffic flows, and vibrations. This predictive capability enables maintenance teams to address issues before they become critical, reducing downtime and repair costs. Deloitte's 2022 report highlights that predictive maintenance can lead to a 5-15% decrease in facility downtime and a 5-20% boost in labor productivity. 

The Norwegian Public Roads Administration monitors the Stavo Bridge in Norway using digital twin technology to detect potential issues. The 3D twin recently detected an anomaly through its sensors and triggered alerts, prompting authorities to divert traffic and begin work on a replacement bridge swiftly. This proactive response, enabled by digital twin technology, prevented potential accidents and showcased its crucial role in infrastructure maintenance and public safety.

Risk Assessment and Mitigation

Risk assessment with digital twins is a crucial aspect of modern urban planning, especially when addressing environmental hazards and population growth. Digital twins provide a comprehensive and dynamic model of urban environments by integrating real-time data from various sources, including weather patterns, topographical maps, and demographic statistics. This information enables urban planners to simulate and analyze potential risks, such as floods, heatwaves, and population surges, and to design effective mitigation strategies.

For example, using digital twins to evaluate flood risk involves simulating rainfall events and analyzing water flow patterns in urban areas. Planners can identify vulnerable zones and assess the impact of different rainfall scenarios on these areas. By integrating this data with existing drainage infrastructure, digital twins can help design more effective drainage systems to handle anticipated water volumes. This proactive planning ensures that urban areas are better equipped to manage flood risks, protecting infrastructure and residents. Additionally, digital twins can model the effects of population growth on housing, transportation, and public services, allowing for more sustainable urban development that anticipates future needs and challenges.

Public participation and stakeholder engagement

Interactive Platforms

A digital twin's interactive features offer a powerful tool for public engagement in urban planning. These platforms allow citizens to visualize and interact with detailed 3D models of their cities, providing a clear and comprehensive view of proposed developments. By making urban planning more transparent and accessible, digital twins help bridge the gap between planners and the public, fostering a collaborative approach to city development.

For instance, residents can use digital twins to view proposed neighborhood changes, such as new parks, road expansions, or high-rise buildings. These interactive models can be accessed via websites or mobile apps, enabling citizens to explore the impacts of these changes from different perspectives. Users can see how new developments affect traffic flow, environmental conditions, and community amenities. Additionally, digital twins can incorporate feedback mechanisms, allowing residents to share their opinions and concerns directly with planners. This feedback can then be used to refine proposals, ensuring that urban developments meet the needs and preferences of the community. Digital twins promote more inclusive and responsive urban planning by engaging the public.

Architects in Trondheim, Norway, were working on an urban development project that would envision strategic changes to the city. As part of the project, they created a 3D version of Trondheim with high-resolution, 360-degree images allowing citizens to view proposed infrastructure modifications and new buildings. Citizens could also add their feedback and comments directly to the model.

Collaborative Planning

Collaborative planning facilitated by digital twins enhances the integration and cooperation between various stakeholders, including government agencies, businesses, and the public. Digital twins provide a unified and interactive platform where all parties can access the same detailed, real-time data and simulations, ensuring that decisions are informed and aligned with shared goals.

For example, digital twins can serve as a central hub for collaboration in planning a new public park. Government agencies can input regulatory requirements and environmental data, while businesses might contribute insights on economic impacts and infrastructure needs. Meanwhile, the public can engage through interactive platforms, visualizing proposed park layouts, amenities, and environmental impacts. They can provide feedback on their preferred urban features or concerns, such as traffic congestion or noise pollution.

Case Studies of digital twins in urban planning

Virtual Singapore

Virtual Singapore is a comprehensive digital twin of the city-state that leverages cutting-edge technology to enhance urban planning, management, and sustainability. It begins with acquiring high-resolution LiDAR scans and detailed aerial imagery to create a 3D model of the city's contours and infrastructure, integrating existing data from government agencies, including building blueprints and real-time traffic data. This geospatial and existing data is synthesized using platforms like Dassault Systèmes' 3DEXPERIENCE City, creating a cohesive 3D model tailored to various planning needs. The model dynamically integrates real-time data from sensor networks monitoring traffic, weather, and energy usage. This real-time integration allows planners to conduct simulations and analyses, providing valuable insights into the potential impacts of changes within the cityscape, thereby aiding informed decision-making. For instance, by considering the roof surfaces of buildings in an area at a specific height, urban planners can estimate the amount of solar energy it can generate if solar panels are installed. 

Helsinki's 3D City Model

Helsinki invested EUR 1 billion in a project called Helsinki 3D+ to create detailed 3D models covering over 500 square kilometers of the city with up to 10 centimeters of accuracy. The project used Bentley's technology, which integrated LiDAR laser scanning and photogrammetry data, enabling advanced city analyses and simulations.

Helsinki's digital twin is vital for energy efficiency planning, infrastructure management, and decision-making for new city development initiatives. This model integrates data from various sources, such as building energy consumption and weather patterns, to simulate and analyze energy usage, allowing planners to identify areas for improvement and optimize efficiency. Additionally, it facilitates collaboration among stakeholders and the public by providing a shared platform for data exchange and analysis, enhancing decision-making, and supporting the development of innovative smart city solutions. 

An open data approach facilitated the efficient sharing of models with stakeholders, promoting digital city development and innovation. With these models, Helsinki can simulate infrastructure, support alternative energy sources, and ensure environmental sustainability, optimizing decision-making and urban progress.

Boston's Smart City Digital Twin

Boston's digital twin is built with data that enables the city to analyze urban development projects and their impact on housing, zoning, and parking within neighborhoods. Planners can also use data from urban heat island studies to visualize environmental factors like 'temperature' concerning buildings, impervious surfaces, and tree canopies.

The digital twin helps stakeholders make informed decisions about the city's planning and development, and modeling of flood risks. Boston officials are also considering integrating data from sensor feeds into maps and models to help offer real-time visualization of city services.