Mobility Digital Twin: Simulation Shapes a Smarter City

A mobility digital twin – what is it and how can it improve transportation?

Many cities continue to approach transportation planning by addressing issues as they arise. Naepo’s approach differs from the conventional model, offering a unique perspective that may serve as a valuable reference point for others.

Naepo, located in South Korea’s Chungnam Province, is designed as a National Innovation City and administrative center. From the outset, digital transformation informed its strategic planning. Simulation was not merely an additional component; rather, it was a foundational element of the system.

Naepo built a mobility digital twin: a virtual model that mirrors real-world transport using live data and simulation tools. This twin assists planners in evaluating concepts prior to construction. It facilitates data-driven decision-making, enhances teamwork, and ensures designs are future-proof.

This article will demonstrate how Naepo used PTV Visum and PTV Vissim to create its digital twin. You will also learn why this is important to projects worldwide: The insights from Naepo are applicable to any city seeking to implement data-driven mobility strategies.

Why a Mobility Digital Twin for Naepo?

The Naepo planning team needed a way to model and evaluate transport systems before physical infrastructure was built, ensuring data-informed decisions from the start.

To meet this goal, the KAIST Mobility Institute deployed PTV Visum and PTV Vissim. Together, these tools provide a multi-scale environment for analyzing demand, simulating multimodal interactions, and testing policy interventions.

The digital twin enables:

• Simulation of daily traffic and multimodal operations.
  
• Policy and infrastructure scenario evaluation.
  
• Testing of emerging technologies like autonomous driving and connected ITS (C-ITS).
  
• Immersive visualization and VR engagement for planners and citizens.

By combining strategic planning and operational modeling, Naepo has moved from reactive management to predictive, data-driven decision-making.

Building the Model: From Data to Deployment

The project’s development followed a structured approach:

Simulation based Assignment Model (PTV Visum)

• Defined 63 zones (46 internal, 17 external).

• Built seed matrices using mobile data and traffic counts.

• Applied matrix adjustment, calibration, and validation to match observed flows.

• Combined OpenStreetMap (OSM) data, desktop analysis, and site surveys for a consistent road network.

Operational Model (PTV Vissim)

• Imported demand and route data from Visum

• Integrated 3D vehicle models, elevation data (GeoTiff)

• Conducted calibration and validation following Operational Modelling Guidelines (OMG)

• Produced realistic 3D micro-simulations to support detailed operational analysis.
  
  The result is a multi-scale digital twin of Naepo that links long-term planning with day-to-day operational insights.

Practical Applications of Naepo’s Digital Twin

Traffic Monitoring and Operations: Integration with Intelligent Transport Systems (ITS) enables real-time traffic monitoring, congestion prediction, and optimization of management schemes.

Policy Evaluation and Urban Planning: Scenario testing supports decisions on road design, pedestrian safety, accident analysis, and policy review – improving transparency and collaboration among stakeholders.

Future Mobility Research and Testing: Naepo serves as a testbed for autonomous driving, C-ITS, and green mobility strategies, including CO₂ estimation and acoustic analysis. The platform supports adaptive smart intersections and multimodal simulations involving vehicles, pedestrians, and micro-mobility.

Education and Stakeholder Engagement: Through 3D visualization and VR environments, the digital twin facilitates training, public communication, and collaborative design review – helping residents and policymakers understand mobility solutions in an accessible way.

Towards Data-Driven Mobility

The Naepo Mobility Digital Twin represents a step forward in how cities plan, operate, and communicate complex transport systems.

By integrating PTV Visum for real-time traffic operation and dynamic route choice, and PTV Vissim for high-resolution micro-simulation, KAIST and Naepo’s planners have built a foundation for:

• Smarter traffic management
• Sustainable urban design
• Evidence-based policy evaluation
• Safe and transparent testing of future mobility technologies

As Naepo evolves, its digital twin will continue to grow – integrating environmental and acoustic data with AI-powered analytics to guide the city toward smarter, carbon-neutral mobility.

Lessons for Global Mobility Planners

Naepo’s success wasn’t just about deploying advanced simulation software – it was about embedding simulation into the culture of planning from day one. For cities elsewhere, the key takeaway isn’t “use Visum and Vissim” – it’s how and when to use them.

So here are my tips for other mobility planners:

• Start Early, Simulate Often: Naepo didn’t wait for problems to arise. Simulation was used before infrastructure was built. Most cities still use modeling reactively – after congestion or safety issues emerge. That’s too late.
  
  
• Break Silos Between Strategy and Operations: Naepo’s planners didn’t treat strategic planning and operational modeling as separate domains. They built a feedback loop between Visum and Vissim. Many cities still isolate these functions, missing out on system-wide insights.
  
  
• Invest in Calibration, Not Just Visualization: The project emphasized rigorous calibration and validation. Too often, cities prioritize flashy visuals over data fidelity. A digital twin is only as good as its assumptions.
  
  
• Use Simulation to Democratize Planning: Naepo used VR and 3D visualization to engage citizens and policymakers. This isn’t a gimmick – it’s a way to build trust and transparency. Simulation should be a public tool, not just a technical one.
  
  
• Think Beyond Traffic: Naepo’s model includes CO₂ estimation, acoustic analysis, and multimodal interactions. If your city’s simulation only models cars, it’s not modeling mobility – it’s modeling yesterday.