Digital Twin for Urban Planning

Experion helps city governments and real estate developers build domain-specific digital twin solutions that convert raw data into actionable intelligence- from traffic modeling to infrastructure lifecycle management.

Cities today face numerous challenges – the most notable one being unplanned urbanization. Other challenges include traffic congestion, climate change, and increasing infrastructure demand. These challenges reveal a planning gap.

It is not uncommon to hear of new residential districts flooding after a storm, or a public transportation project shooting over budget because underground utility conflicts were discovered only during excavation. These incidents are not isolated failures but an effect of making decisions without adequate information.

Now, suppose these conflicts could be detected beforehand. Suppose flood risk could be modeled against a proposed drainage layout before it’s built. All within a virtual replica of the city itself. That’s what urban digital twins make possible. The ability to simulate and optimize urban environments before physical work begins.

A digital twin for urban planning is designed to bridge the gap between the complexity of modern cities and the limitations of traditional planning tools. This blog explores how this data-driven approach is changing urban governance, and what city leaders and developers need to know before investing in it.

 

Key Takeaways

• A digital twin is a real-time, data-synchronized virtual model of a physical city or district.
• Urban digital twin platforms have moved well past visualization. Mature deployments run predictive simulations that answer questions like what happens to traffic if this road closes? What does flood exposure look like in 2040 under two different development scenarios?
• Cities like Singapore, Helsinki, Las Vegas, and Amaravati have operational city digital twins today. The outcomes are measurable: shorter planning cycles, fewer construction overruns, and faster emergency response.
• For AI-powered digital twins, the model learns and improves as live data from IoT sensors, satellite feeds, and city systems flows in.
• When evaluating platforms, data integration depth and simulation accuracy matter more than how polished the 3D visualization looks.

 

What Is a Digital Twin for Urban Planning?

In the 1990s, planners worked with physical scale models. In the 2000s, GIS software replaced most of those with digital maps. BIM (Building Information Modelling) added a third dimension, letting architects create detailed replicas of individual buildings before construction. Each tool was an improvement on what came before. None of them could predict what would happen next.

Thus, Digital Twins fill the functional gap.

Defining Digital Twin Technology in an Urban Context

A digital twin for urban planning is a virtual replica of a city, district, or infrastructure network that stays synchronized with its physical counterpart through continuous data feeds. It doesn’t just show what a city looks like today. It shows what’s happening currently. This allows urban planners to model what could happen under different conditions.

Data comes from IoT sensors embedded in roads and bridges, traffic cameras, utility meters, weather stations, satellite imagery, and administrative records like zoning files and permit applications. When these streams converge in one platform, planners gain a feature- a city they can actually test.

Digital Twin vs. Traditional Urban Planning Tools

How do Digital Twins differ from the traditional urban planning tools already available?

GIS maps show static snapshots of what already exists. It is useful for understanding current conditions, but is unable to answer ‘what if’ scenarios.

BIM models are detailed but isolated individual buildings and not a connected city.

CAD is a design tool with no live connection to anything happening on the ground.

A digital twin can pull all three together, adding a live data layer and a simulation engine that none of them have.

Feature	GIS	BIM	CAD	Digital Twin
Real-time data	No	No	No	Yes
Scenario simulation	Limited	Limited	No	Full
City-scale modeling	Yes	No	No	Yes
Predictive analytics	No	No	No	Yes (AI-powered Digital Twins)
Live infrastructure monitoring	No	No	No	Yes

The question GIS, BIM, and CAD can’t answer is: “What happens if we add 50,000 residents to this district?” This is exactly what a digital twin is built to model.

 

Types of Urban Digital Twin Platforms

Not all digital twin platforms are built for the same job. The three main categories map to distinct planning problems, and choosing the wrong type for your use case is a surprisingly common mistake.

Infrastructure-Focused Digital Twins

These platforms focus on physical assets: road networks, bridges, tunnels, water systems, power grids, and public buildings. Their primary value lies in catching deterioration before it becomes an emergency.

A city with an infrastructure digital twin can monitor bridge loads in real time, model how heavy freight traffic degrades specific road sections over 5 years, and direct maintenance budgets to actual risk rather than calendar schedules.

Several European cities have caught bridge stress problems 18 to 24 months before structural risk materialized, avoiding emergency closures and the costs that follow.

Environmental and Climate Digital Twins

Climate risk has changed what urban planning needs to model. Cities facing sea-level rise, intensifying storms, or urban heat islands need tools that go beyond historical data.

Environmental digital twins incorporate factors such as climate projections, elevation data, vegetation maps, and hydrological models to simulate city performance under specific future scenarios. These scenarios include 15% more annual rainfall, a 2°C temperature rise, and a once-in-50-year storm hitting drainage infrastructure designed for 1980 rainfall patterns.

National governments increasingly require these tools as part of planning approvals, rather than relying on voluntary use by forward-thinking cities.

Integrated  Smart City Digital Twin

Integrated smart city digital twins aim to model a city’s full complexity: people, infrastructure, environment, economy, and governance systems interacting simultaneously.

A prime example would be  Virtual Singapore, which started in 2014.  It combines 3D models of every building in the city-state with demographic, environmental, and infrastructure data. Planning decisions, from school placements to large-scale solar rollouts, are tested in the twin before any physical action is taken.

From pilot to production, explore how Experion can help deploy city-scale digital twins.

 

From pilot to production, explore how Experion can help deploy city-scale digital twins

 

Core Architecture: How AI-Powered Digital Twins Function

Understanding the core architecture matters here because it determines which vendor questions to ask and which impressive demos deserve skepticism.

• Data Integration Layer: IoT sensors, BIM models, satellite imagery, traffic APIs, utility SCADA systems, and administrative databases- all of it needs to be normalized and mapped to a common spatial coordinate system. This is unglamorous infrastructure work. It’s also consistently the layer that blows project timelines. Nearly every digital twin deployment that has run over schedule has stumbled here and not in the simulation engine.
• AI and Simulation Engine: This is where digital twins used in urban planning and infrastructure depart from traditional city modeling. AI models trained on the city’s own historical data predict outcomes rather than just describe current states. Machine learning models can forecast traffic volumes, estimate energy demand under different density scenarios, and predict pavement degradation based on load and climate exposure. Accuracy improves over time as real-world outcomes feed back into the model. Early deployments have rougher predictions than mature ones. This is worth knowing when you’re interpreting pilot results.
• Visualization and UX Layer: The visualization layer forms the 3D environment where planners, developers, and sometimes citizens interact with the twin. It matters for communication, though not a measure of platform intelligence.
• Feedback Loop: As planning decisions are implemented, the platform compares predicted and actual outcomes and adjusts its models. A city that’s been running a digital twin for five years has a more meaningful and accurate simulation engine than it did at launch. That improvement is real, but it doesn’t happen overnight.

 

Technologies Powering Digital Twins

A digital twin is not just one product, but several technologies working together.

• Internet of Things (IoT): IoT sensors keep digital twins connected to the real city. Sensors embedded in roads, bridges, utility pipes and air quality monitors feed a continuous stream of readings into the platform. The quality of data from the IoT layer determines the accuracy of the simulations.
• Artificial Intelligence (AI) and Machine Learning: Without AI, a digital twin shows you what a city looks like today. With it, you can ask what’s likely to happen next. ML models are trained on city-specific data. It helps officials forecast traffic volumes, catch infrastructure stress before it escalates, and compare interventions by cost. Worth knowing: launch accuracy is rougher than three-year accuracy. The model improves as real outcomes feed back into it.
• Geographic Information Systems (GIS): GIS is the spatial backbone of digital twin technology. Every data point in the twin needs to be tied to the same coordinate system before it can be analyzed alongside anything else. This includes sensor readings, planning records, and satellite images. Most cities already have GIS infrastructure and digital twins built on top of it.
• Cloud computing: A mature urban twin can process terabytes of sensor data daily and run heavy simulations on demand. Most cities can’t support that on local infrastructure. Cloud computing also keeps the platform running during emergencies and provides the processing capacity to run simulations on demand.
• Big data analytics: City data comes from dozens of systems built decades apart in incompatible formats. The data layer normalizes all of it, stores continuous sensor feeds without degrading performance, and returns complex spatial queries fast enough that planners actually use it. A slow or fragile data layer gets abandoned regardless of how good the simulation engine is.
• 5G connectivity: Most sensors don’t need 5G. But in certain applications, such as live traffic adjustment and emergency coordination, timing is crucial. They are sensitive to latency in ways older wireless standards struggle with. 5G connectivity thereby removes the network constraints that previously limited real-time sensing.

 

Industry Applications: Where Digital Twins Deliver the Highest ROI

Digital Twin  Urban Planning and Infrastructure

Infrastructure management is where digital twin technology has the longest track record. Most applications include the following:

• Road and bridge lifecycle management: Bridge structures are fitted with sensors that feed real-time load and stress data to the twin. AI models can compare current readings against design specifications and historical decay rates. If the values differ significantly, they are flagged for maintenance before further structural risk develops.
• Underground utility mapping and maintenance: Utility conflicts are one of the most common causes of construction delays and cost overruns. If a development hits an unmapped pipe or cable during excavation, projects are further delayed, i.e., a six-month project becomes a twelve-month project. A digital twin that integrates underground utility records with proposed development footprints can detect these conflicts at the planning stage.
• New district master planning: When designing a new residential or commercial district, digital twin technology enables planners to test multiple layout configurations against parameters such as traffic, drainage, sunlight, energy demand, and social equity metrics simultaneously. All of this can be done before committing to a design and starting the approvals process.

Digital Twin City Examples

• Singapore’s Virtual Singapore:

Singapore’s digital twin project, Virtual Singapore, is used by the government for day-to-day decision-making. It combines detailed 3D models of every building in the city-state with demographic, environmental, and infrastructure data. It’s been used to plan solar panel deployments across thousands of rooftops and model sea-level rise impacts on coastal districts. The insights obtained from the project also helped optimize emergency response routing.

• Helsinki’s Helsinki 3D+:

Helsinki, the capital of Finland, is famous for deploying a digital twin for urban planning. Named the 3D+ platform, it integrates building data from the city’s BIM library. Terrain models, population data, and climate projections are collected. Furthermore, Helsinki has used it to analyze urban heat island effects in specific neighborhoods and model the cooling impact of different greening interventions. The 3D+ platform also integrates layers of data, such as:

– 3D mesh: A detailed representation of the terrain and infrastructure

– Urban Data Model: Offers data about the infrastructure and environmental factors.

– Energy and Climate Atlas– Contains details on the energy consumption, heating systems across various buildings  and data on water usage.

The digital twin platform has improved sustainability and its carbon neutrality targets.

• Las Vegas: Las Vegas deployed a city digital twin focused on traffic flow. The platform pulls data from thousands of sensors and cameras to model vehicle movement in real time. Signal timing across hundreds of intersections adjusts dynamically based on the twins’ predictions. This leads to shorter average commute times on key corridors.
• Amaravati: India’s planned capital of Andhra Pradesh used digital twin technology from day one. An entirely new city was modelled before permanent construction began. Road layouts, utility networks, flood drainage, and public space distribution were all tested in the twin before physical development started. It’s one of the clearest examples of what digital city planning looks like when the technology is used from the start rather than retrofitted into an existing city. By using digital twin models, government officials can manage permitting and construction progress.

Digital Twin for Real Estate and Mixed-Use Development

Private developers use digital twins to support planning applications. Using Digital Twins, a developer can prove that a high-density mixed-use project won’t create any adverse impact on neighbouring properties. This can be done by presenting direct simulation evidence instead of claims. That shortens the approval process and reduces the risk of objections derailing the timeline.

For large urban projects, the ROI is direct: faster approvals mean earlier revenue.

 

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Other High-Impact Verticals

• Public transit and mobility planning. Commuter flow can be modelled under different density and employment scenarios to optimize routes and service frequency before committing capital to physical infrastructure.
• Emergency response and disaster management. Simulating evacuation scenarios in advance to find bottlenecks, optimize emergency service placement, and stress-test response plans against different incident types.
• Environmental compliance. Tracking green space coverage, tree canopy percentage, and permeable surface ratios against regulatory targets. This helps in turning annual manual audits into a live data exercise.

Experion combines deep engineering expertise with applied AI research to build urban digital twin systems that operate at city scale and not proof-of-concept pilots that stall after the demo.

 

How are Digital Twins Used in Urban Planning and Infrastructure?

The clearest way to understand the technology is through the specific problems it solves.

1. Scenario Simulation: Before a major development breaks ground, planners can test its full impact in the twin. A new skyscraper’s effect on wind patterns, sunlight access for neighboring buildings, and street-level traffic can all be modeled at once. Changes that previously required multiple rounds of expert consultation and months of review can be evaluated in days.
2. Smart Mobility: City digital twins optimize public transit routes based on real commuter demand data instead of historical estimates. Cities that have adopted this approach have reduced peak congestion on specific corridors by 15 to 20% without adding any physical infrastructure. The gains come from adjusting routes and signal timing based on what’s actually happening.
3. Environmental Resilience:
   a. Urban Heat Island (UHI) Mitigation: Densely built environments retain significantly more heat than surrounding areas. That’s not just uncomfortable, it’s also a public health problem in many cities. Environmental digital twins can identify hotspot locations and simulate the cooling effects of specific interventions, such as green roofs, additional tree canopy, and reflective paving. Planners compare costs and impacts before committing the budget to any single approach.
   b. Flood Management: Real-time water flow simulations let cities model drainage system performance during heavy rainfall before an actual crisis occurs. Rather than waiting for a flood event to reveal where the system failed, planners run the scenario in the twin and redesign drainage infrastructure proactively.
4. Infrastructure Health: Digital Twin for Urban Planning enables Predictive maintenance for bridges, tunnels, and power grids based on continuous sensor data. The digital twin flags developing problems. This leads to maintenance teams responding to actual risk rather than a maintenance calendar.

 

Why Businesses and Governments Must Invest in Digital Twin Technologies?

Digital twins in healthcare are hailed as revolutionary, enabling predictive diagnostics and real time patient monitoring. Additionally, Digital Twin for smart manufacturing has been reported to have optimized production at scale.

Without a doubt, Digital Twin for urban planning is fully functional as well. That question has been settled by Singapore, Helsinki, Las Vegas, and a growing list of cities running operational deployments. The question most budget committees are actually wrestling with is whether the investment can be justified against other competing priorities.

Cities competing for investment, talent, and residents need infrastructure that performs better than their peers’. Digital twin technology gives planning departments the tools to make faster, better-evidenced decisions compared to cities still working from GIS maps and static impact assessments. That’s a compounding advantage. The cities that build this capability now are making better decisions year after year, while others are still running the same manual processes.

The competitive gap is already visible. Singapore’s ability to model solar deployment across 10,000 rooftops and commit to a phased rollout plan came directly from Virtual Singapore. Helsinki’s carbon neutrality roadmap is grounded in digital twin modeling.

On the financial side, McKinsey research on smart city infrastructure indicates data-driven planning approaches reduce infrastructure lifecycle costs by 10 to 20% over a 20-year horizon. For a city managing $5 billion in infrastructure assets, that range represents $500M to $1B in avoided costs. For real estate developers, faster planning approvals and fewer construction surprises directly translate into higher project margins on every development.

Public-private partnerships also work better when both parties use the same data. Digital twins create a shared, live view of a city’s infrastructure. This leads to negotiations about responsibilities and performance standards being more grounded in evidence than in assumptions.

 

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Benefits of Using Digital Twin in Urban Planning

Smarter infrastructure decisions before construction starts. Running 30 development scenarios in a digital twin costs a fraction of what it takes to discover a single design revision during construction.

• Fewer cost overruns: Infrastructure projects regularly exceed budgets due to conflicts discovered during excavation. This includes unmapped utilities, unexpected soil conditions, and traffic management requirements that nobody fully modeled. Digital twin simulation surfaces many of these at the planning stage.
• Better climate resilience: Cities can model a 100-year storm against their current drainage system, identify the most vulnerable areas, and prioritize upgrades based on actual risk.
• More equitable development: Data-driven tools show who gains green space, who loses sunlight, and whose neighborhood absorbs the traffic from a new development. That information doesn’t guarantee better decisions, but it makes consistently bad ones harder to justify solely through process.
• Faster regulatory approvals: When simulation evidence is already part of the planning application, committees need fewer expert consultations and fewer revision cycles. Approval timelines come down.
• More substantive community consultation: When residents can see what a proposed development means for their street, public consultations produce more useful feedback and less blanket opposition.

 

How to Create a Digital Twin for Urban Planning?

The most common mistake is trying to do too much at once. A full-city digital twin built from scratch is a reliable way to exhaust the budget before delivering anything.

Start with one district or one clearly defined problem.

Step 1: Define objectives and scope: Pick a specific planning problem. This might be reducing infrastructure maintenance costs. Shortening planning approval timelines. Improving flood resilience in one high-risk district. Each objective points to a different platform configuration and data integration priority.

Step 2: Collect and integrate data: Audit your existing data sources. Identify which systems hold the data you need, in what format, and how up-to-date they are. This step consistently takes longer than planned. Traffic data, utility records, and planning files typically live in separate departments on systems that weren’t built to share data.

Step 3: Build 3D models and simulations: Layer spatial data into a 3D environment that reflects the current state of the city or district. At this stage, accuracy matters more than visual quality. A platform that looks impressive but runs on stale or incomplete data isn’t useful for planning decisions.

Step 4: Implement AI and analytics: Connect AI and machine learning models to the integrated data environment. This is where the platform gains predictive capability: forecasting outcomes across different scenarios, flagging emerging infrastructure issues, and comparing intervention options against cost and impact metrics.

Step 5: Continuous monitoring and updates: A digital twin that isn’t maintained loses its value faster than expected. Define ownership upfront, who keeps the data feeds current, who updates the model when infrastructure changes, and who manages vendor relationships.

Common Pitfalls in Digital City Planning

• Buying a 3D visualization tool and calling it a digital twin: A polished 3D city model with no live data connection is an expensive static render. Insist on seeing live data integration during vendor evaluation.
• Underestimating data integration complexity: Connecting the twin to the legacy systems that hold your city’s operational data typically consumes 40 to 60% of the total implementation effort. If your project plan doesn’t show that, it needs to be revised before you start.
• Skipping governance: No defined owner means the digital twin drifts out of sync with reality within months of launch. Define who owns it before deployment begins.

 

Challenges in Implementing Urban Digital Twins

• Data privacy and security: Real-time monitoring of urban spaces means collecting data about how people move through the city. The legal requirements around data minimization, consent, and storage vary significantly by country and region. These need to be built into the platform architecture from day one.
• High upfront costs: A district-level pilot on a purpose-built platform typically costs $500K to $2M. A full-city deployment with deep data integration usually runs $5M to $30M or more over several years. The upfront costs are high.
• Legacy system integration. Most city data lives in systems that weren’t designed to communicate with each other. Traffic management platforms, utility SCADA systems, planning databases, and permit records often run on incompatible architectures built decades apart. This is consistently the hardest engineering problem in digital twin deployment and the most underestimated.
• Skills gaps: Running a mature digital twin requires data scientists, platform engineers, and GIS specialists with experience in AI. Most city planning departments don’t have these roles. The amount of ongoing support a vendor provides matters more than most procurement processes acknowledge. During vendor evaluation, ask specifically about year-two and year-three support, not just implementation.
• Data accuracy over time. A digital twin is only as reliable as its data. Sensor failures, outdated records, and inconsistent collection practices degrade simulation accuracy when data quality is not actively managed. This needs to be an operational responsibility, not an afterthought.

 

Future of AI City Planning and Digital Twin Cities

Current deployments run scenarios when planners request them. The platforms emerging now monitor thousands of data points continuously and surface recommendations proactively. It can flag infrastructure stress, emerging demand patterns, and maintenance needs before anyone has thought to check. Early versions of this are already running in a handful of cities.

Alongside that, digital twin urban planning is moving from standalone planning applications to the connective layer between smart city systems. Functions such as Traffic management, energy grids, emergency services, public transit, and environmental monitoring are linked through a shared digital twin. This enables the infrastructure to begin adapting to conditions rather than just responding to instructions. Cities that are building this integration now are laying the groundwork that others will take years to replicate.

Parallelly, some routine infrastructure decisions are already being automated. Traffic signal timing is adjusted; transit services are rerouted during incidents, maintenance dispatches when sensor readings cross a threshold. All these are happening in live deployments today. Full autonomous management of complex infrastructure is further out and raises questions that urban planners, lawyers, and ethicists are still working through.

The data that digital twins accumulate also opens up something that cities have never really had: the ability to allocate services based on actual community need rather than political intuition. Transit frequency, maintenance prioritization, and green space investment. All of these can be modeled at a neighborhood level when the underlying data is detailed enough.

The gap between cities using digital twins and cities that aren’t widening every year. Singapore, Helsinki, Las Vegas, and Amaravati are building institutional knowledge and data history that compound. The cities investing now will have a five-year head start on everyone who waits until the technology feels more settled.

 

Analytics and Optimization: Getting Value from Your Urban Digital Twin

Visualization gets digital twins approved in budget meetings. Analytics is what justifies the ongoing investment after the meeting ends.

1. Scenario planning at speed: Running 50 infrastructure scenarios before committing capital budget funds is different from running three scenarios over six months. Planners with rapid scenario access start asking questions they couldn’t previously afford to ask. That changes the quality of decisions and not just the speed.
2. Continuous improvement: Every time a planning decision is implemented, the twin compares predicted outcomes with actual outcomes and updates its models. A platform that’s been running for five years has substantially better simulation accuracy than it had on day one. That accuracy compounds in value over time.
3. Measuring what matters: The clearest ROI metrics in urban digital twin deployments are planning cycle time reduction, construction cost avoidance from earlier conflict detection, maintenance cost reduction from predictive rather than reactive management, and improved emergency response. Cities with mature programs can tie these numbers directly to specific platform outputs.

 

Conclusion

The question is no longer whether digital twin technology works. Several global use cases have answered that question. Cities such as Singapore, Helsinki, Las Vegas, Amaravati, and dozens of others have moved from pilot to operational. They’re making infrastructure decisions faster, catching maintenance problems earlier, and handling planning applications more efficiently, not because they have more resources, but because they have better information when making decisions.

A practical entry point would be to begin with pilot urban digital twins in certain sectors. This includes transport or emergency disaster response. Cities can demonstrate the benefits at a smaller scale. Another approach would be to implement digital twins in special development zones.

By starting in a phased manner, cities can unlock the full potential of digital twins.

 

Frequently Asked Questions (FAQ’s)

• What is a digital twin in urban planning?

A digital twin in urban planning is a virtual model of a city, district, or infrastructure network that stays synchronized with its physical counterpart through live data feeds from IoT sensors, traffic systems, and city databases.

• How does a digital twin help smart cities?

A digital twin gives smart city systems a shared operational brain. Rather than managing traffic, energy, emergency services, and public transit in separate silos, a city’s digital twin connects them in a single live environment. City managers can monitor conditions across all systems simultaneously and catch problems before they escalate.

• What technologies are used in digital twins?

Technologies used in digital twins include GIS and BIM systems for providing spatial foundations. AI and ML models power the prediction engine. Cloud computing handles data storage, and lastly, 3D visualization platforms make the output accessible.

• Are digital twins expensive to implement?

A focused district-level pilot on a purpose-built platform typically runs $500K to $2M. A full-city deployment with deep data integration across multiple municipal systems usually costs $5M to $30M or more over a multi-year timeline.

• Which cities are using digital twin technology?

Virtual Singapore is the most well-known. Helsinki, Finland, uses its Helsinki 3D+ platform for climate modeling and urban planning, tied to its carbon-neutrality targets. Las Vegas runs a city digital twin focused on real-time traffic signal optimization. Amaravati in India used digital twin technology to design an entirely new city before construction began.

• How is AI used in city planning through digital twins?

AI powers the simulation and prediction layer that separates a digital twin from a visualization tool. Machine learning models trained on historical and real-time city data forecast traffic volumes and estimate the population impact of rezoning decisions. Without AI, a digital twin shows you what’s happening. With it, planners can model what’s likely to happen next and test interventions before committing to them.

• What is the difference between a digital twin and a GIS map?

A GIS map shows what exists at a point in time. A digital twin shows what’s happening now and lets you model what could happen next. GIS is a descriptive tool, whereas a digital twin is a predictive one. The practical difference is real-time data synchronization and AI-powered scenario simulation – neither of which a GIS map provides.

• What industries benefit most from digital twin technology?

Urban planning and municipal infrastructure management have the clearest returns. In addition, real estate developers use digital twins to accelerate planning approvals. Energy utilities model grid load and failure scenarios. Transportation networks optimize routing and maintenance. Emergency services use simulation for disaster response planning. Healthcare systems have used digital twins to model hospital capacity and patient flow.

• Is digital twin technology the same as the metaverse?

No. The metaverse is a consumer-facing virtual social environment designed for interaction and entertainment. A digital twin is an operational tool for managing physical assets and informing real planning decisions. They may share some underlying visualization technology, but their purpose and architecture are fundamentally different.

With over 20 years of product engineering experience, Experion brings the technical depth and municipal domain knowledge to help cities and developers build digital twin systems that work in production not just in demos