Synthetic Users in More Industries
Synthetic Users in Smart Cities
By Sherry Jones (September 2025)
Cities are revolutionizing urban planning through virtual populations - artificial commuters, travelers, and citizens that mimic real behavior patterns. These synthetic users enable "what-if" experiments without privacy concerns, allowing planners to test everything from traffic signals to emergency responses in digital environments before implementing changes in the real world.
Synthetic Population and Transportation and Mobility Simulation
Cities increasingly build virtual models of travel by generating synthetic populations that mimic real behavior. Sidewalk Labs' Replica platform runs location data through models based on a "synthetic population" so planners can query mobility patterns without tracking anyone.
Portland's transit agencies piloted Replica by feeding in de-identified phone data to create a "mock modeled populace" of drivers, cyclists, and transit riders. Columbia University's COSMOS testbed in New York City creates real-time traffic digital twins, integrating IoT sensors and simulations to optimize signal timing and predict congestion.
01
Data Collection
De-identified phone and sensor data feeds synthetic population models
02
Virtual Testing
Adjust signal algorithms or add bus lines to see population response
03
Real Implementation
Deploy tested strategies like adaptive traffic lights on actual streets
Synthetic Citizens and AI-Powered Smart City Infrastructure
Artificial intelligence, particularly through the use of synthetic users as citizens and data, is revolutionizing urban planning. These AI-powered simulations allow city planners to model and test various infrastructure designs and interventions in a virtual environment, leading to more efficient, sustainable, and responsive smart cities that better serve their inhabitants.
Urban Monitoring & Alerts
AI systems continuously monitor urban areas, identifying anomalies and alerting authorities for rapid response to incidents or emergencies.
Predictive Policing
Utilizing data analytics and machine learning, AI helps predict potential crime hotspots, enabling more proactive and efficient resource deployment.
Traffic Optimization
AI monitors and predicts road conditions to improve traffic flow, reduce congestion, and optimize signal timing, leading to smoother commutes.
Efficient Waste Management
AI algorithms optimize trash collection routes and schedules, reducing operational costs and environmental impact while improving city cleanliness.
Architectural Enhancements
AI contributes to sustainable urban planning and architectural improvements, designing smarter buildings and infrastructure for energy efficiency and livability.
Citizen Engagement & Business Interactions
AI-powered platforms facilitate better communication between citizens, government, and businesses, enhancing participation and service delivery.
AI for Improved Services
Embedding AI into various public services, from healthcare to education, optimizes resource allocation and delivers more personalized and effective solutions.
Advanced Water Treatment
AI monitors water quality, predicts maintenance needs, and optimizes treatment processes, ensuring safer and more efficient water management systems.
Synthetic Citizens for Urban Planning and Infrastructure
Synthetic citizens play a key role in citywide digital twins for planning and development. In Florida, the Orlando Economic Partnership partnered with Unity to build a 3D twin of the entire region covering 800 square miles, layering in infrastructure, land use, and environmental data.
Orlando Digital Twin
800 square miles of virtual city allowing simulation of new transit routes and flood mitigation scenarios
Chattanooga Model
Uses sensor and traffic data to reroute vehicles preemptively during events
Boston Planning
Virtual models predict how proposed housing or roads affect congestion, noise, and air quality
As Hurtado and Gomez note, smart city digital twins let planners "experiment with solutions to meet complex problems" in a controlled environment, avoiding costly real-world trial-and-error.
Energy and Utilities Management
Utilities use synthetic modeling to forecast demand and stress-test the grid. Researchers create synthetic load profiles to train AI on rare events like transformer failures, generating artificial time series of household power usage that follow realistic patterns.
NYU's C2SMART center envisions an energy twin that simulates citywide electricity consumption and distribution in real time, where renewable inputs and variable usage help planners identify weak points and test demand-response strategies.
Real Data Input
Meter data and renewable sources feed the system
Synthetic Scenarios
AI generates artificial load profiles and fault conditions
Grid Optimization
Test reliability and plan upgrades before outages occur
Public Safety and Emergency Response
Synthetic people are used to practice disaster responses without risk to real citizens. Orlando's digital twin incorporates live public safety data including hurricane paths and evacuation status, allowing officials to "model crisis scenarios" and pre-position resources in advance.
Crisis Detection
Live data feeds disaster scenarios into the system
Synthetic Crowds
Virtual populations simulate evacuations and shelter use
Response Planning
Authorities adjust evacuation routes and resource allocation
Protocol Refinement
Fine-tune protocols for police, fire, and medical services
Virtual drills can reveal if synthetic populations bottleneck at particular exits during floods, allowing authorities to adjust evacuation routes before real emergencies occur.
Citizen Engagement and Services
City agencies apply synthetic users to improve services and outreach. Some municipalities create "citizen digital twins" - synthetic profiles of typical residents to analyze how people access government programs or transit.
Pittsburgh's Allegheny County partnered with the Urban Institute to generate synthetic health, housing, and mobility records, coordinating care without exposing personal data. City IT departments can simulate thousands of digital citizens engaging with websites or apps to refine online portals before public rollout.
1
Profile Creation
Generate synthetic citizen profiles representing typical residents and their service needs
2
Service Testing
Test government websites, apps, and programs with virtual users before launch
3
Privacy Protection
Coordinate services and analyze patterns without exposing real personal data
Private Sector and Commercial Applications
Technology firms offer turnkey synthetic-user platforms for "SimCity"-style analyses. Companies like Aaru and Replica let organizations input policies and observe impacts on virtual populations. Google, Amazon, and Meta already utilize synthetic users to test interface changes and marketing campaigns.
Commercial Platforms
Startups like Aaru and Replica provide synthetic population analysis tools for policy testing
Virtual Research
Companies like Viewpoints.ai create AI-driven respondents for virtual focus groups and interviews
Tech Giants
Major platforms use synthetic users to test user scenarios and interface changes at scale
These commercial advances mean cities can increasingly purchase or partner to obtain sophisticated synthetic testing environments for urban planning and service delivery.
Future Directions and Speculative Use Cases
Synthetic users in smart cities could become even more lifelike and interactive. Cities might use AI avatars of transportation planners to forecast traffic under various policies, or virtual advocacy groups to test public reaction to proposed changes.
1
Digital Humans
AI-driven avatars embodying real expertise for policy consultation
2
Virtual Town Halls
AI-powered public meetings staffed by synthetic citizens
3
VR/AR Integration
3D twins with animated virtual pedestrians for immersive planning
4
Climate Adaptation
Sophisticated testbeds for climate scenarios and entertainment planning
The Delphi platform can ingest a person's writings and create a conversational "digital version" of them. In the city context, leaders might someday consult synthetic urbanists or have AI-powered town halls staffed by virtual citizens.
Implementation Challenges and Considerations
Data Privacy
Balancing realistic synthetic populations with privacy protection while ensuring models accurately reflect real citizen behavior patterns
Model Accuracy
Ensuring synthetic users truly represent diverse populations and don't perpetuate biases in urban planning decisions
Technical Infrastructure
Building computational capacity and expertise needed to run sophisticated city-scale simulations effectively
Public Trust
Maintaining transparency about synthetic user applications while building citizen confidence in AI-driven planning
As cities increasingly adopt synthetic user technologies, addressing these challenges will be crucial for successful implementation and public acceptance of AI-driven urban planning initiatives.
The Future of Urban Intelligence
Synthetic users represent a fundamental shift in how cities plan, test, and optimize urban systems. From transportation networks to emergency response, these virtual populations enable unprecedented experimentation without real-world risks or privacy concerns.
800
Square Miles
Orlando's digital twin coverage area
4,548
Store Closings
Synthetic models help predict retail impacts
100%
Privacy Protected
No real citizen data exposed in testing
As AI improves, cities will have ever-more sophisticated virtual testbeds where digital citizens populate scenarios from climate adaptation drills to entertainment district planning, years before changes hit real streets. The future of urban planning is digital, intelligent, and powered by synthetic populations that help create better cities for everyone.
References
[1] Miller, B. (2021, April 21). Replica, Dealer in “Synthetic” Urban Data, Raises $41M. Government Technology. https://www.govtech.com/biz/replica-dealer-in-synthetic-urban-data-raises-41m
[2] Kaye, K. (2019, May 28). Portland quietly launches mobile location data project with Sidewalk Labs. GeekWire. https://www.geekwire.com/2019/portland-mobile-location-data-project
[3] [4] Reid, R. L. (2022, September 21). In NYC, digital twin project tackles traffic. Civil Engineering Magazine. https://www.asce.org/publications-and-news/civil-engineering-source/article/2022/09/in-nyc-digital-twin-project-tackles-traffic
[5] Kaye, K. (2019, May 28). Portland quietly launches mobile location data project with Sidewalk Labs. GeekWire. https://www.geekwire.com/2019/portland-mobile-location-data-project
[6] Reid, R. L. (2022, September 21). In NYC, digital twin project tackles traffic. Civil Engineering Magazine. https://www.asce.org/publications-and-news/civil-engineering-source/article/2022/09/in-nyc-digital-twin-project-tackles-traffic
[7] Orlando Economic Partnership. (2022, April 14). Orlando to Unveil World’s First Digital Twin of an Entire Region. Orlando.org News. https://news.orlando.org/blog/orlando-to-unveil-worlds-first-digital-twin-of-an-entire-region
[8] U.S. Government Accountability Office. (2023). Science & Tech Spotlight: Digital Twins—Virtual Models of People and Objects. https://www.gao.gov/products/gao-23-106453
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[11] Hurtado, P., & Gomez, A. (2021, April 1). Smart City Digital Twins Are a New Tool for Scenario Planning. Planning (APA). https://www.planning.org/planning/2021/spring/smart-city-digital-twins-are-a-new-tool-for-scenario-planning
[12] Stone, A. (2024, July 18). Synthetic Data Supports State and Local Government AI Initiatives. StateTech Magazine. https://statetechmagazine.com/article/2024/07/synthetic-data-supports-ai-initiatives-for-municipalities-perfcon
[13] (n.d.) Digital Twin: Research at C2Smarter Center. C2SMART-ER. https://c2smarter.engineering.nyu.edu/digital-twin/
[14] Farrell, R. (2025, June 5). Digital Twin Cities: How Local Governments Are Using Simulations to Shape Smarter Communities. GovLoop. https://www.govloop.com/community/blog/digital-twin-cities-how-local-governments-are-using-simulations-to-shape-smarter-communities
[15] Stone, A. (2024, July 18). Synthetic Data Supports State and Local Government AI Initiatives. StateTech Magazine. https://statetechmagazine.com/article/2024/07/synthetic-data-supports-ai-initiatives-for-municipalities-perfcon
[16] Farrell, R. (2025, June 5). Digital Twin Cities: How Local Governments Are Using Simulations to Shape Smarter Communities. GovLoop. https://www.govloop.com/community/blog/digital-twin-cities-how-local-governments-are-using-simulations-to-shape-smarter-communities
[17] Stone, A. (2024, July 18). Synthetic Data Supports State and Local Government AI Initiatives. StateTech Magazine. https://statetechmagazine.com/article/2024/07/synthetic-data-supports-ai-initiatives-for-municipalities-perfcon
[18] Mart, A. (2025, July 21). What Are Synthetic Users and How Do They Work? M1-Project Blog. https://www.m1-project.com/blog/what-are-synthetic-users-and-how-do-they-work
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