City of Landau in der Pfalz brings traffic management into the future

What if they could predict emissions before they rise and take action before environmental targets are impacted? And what if they could test infrastructure changes in a virtual environment before making adjustments on real roads?

In the German city of Landau in der Pfalz, this is no longer a vision for the future — it is reality.

At the start of 2026, SWARCO has deployed a fully operational Digital Twin that continuously mirrors the city's traffic network, providing real-time insights, short-term forecasts, and scenario-based decision support. Unlike many Digital Twin initiatives that remain at the concept or pilot stage, Landau in der Pfalz's solution is live, integrated into daily operations, and actively supporting traffic management decisions.

Serving a city of approximately 50,000 inhabitants, the Digital Twin covers around 40 signalized intersections and a road network of roughly 2,000 modelled road segments. Most importantly, it demonstrates how cities can unlock significant value from the infrastructure they already have.

The foundation of the Digital Twin is remarkably simple: use the data already available.

Instead of relying on external data providers or introducing new sensor networks, the system utilizes information from existing traffic signals and loop detectors connected to SWARCO's cloud-based traffic management platform. Traffic counts and signal data are continuously collected and fed into the Digital Twin, creating a live connection between the physical road network and its virtual counterpart.

This approach enables cities to maximize the return on their existing infrastructure investments while minimizing implementation complexity.

The Digital Twin creates a detailed, lane-level representation of Landau in der Pfalz's road network. Every road segment, lane connection, signal timing plan, and traffic control strategy is replicated within the virtual environment.

Unlike traditional traffic simulations that are used for one-off studies, this model operates continuously. Real-world traffic measurements are constantly used to update the simulation, ensuring that the virtual network remains synchronized with actual traffic conditions around the clock.

Traffic management has traditionally been reactive. Operators identify congestion, delays, or incidents and then respond after the problem has already affected road users.

The Digital Twin changes that approach.

Using AI-based forecasting combined with microscopic traffic simulation, the platform continuously predicts traffic conditions up to one hour into the future. Every five minutes, the system generates new forecasts and calculates key performance indicators across the entire network.

These include:

• Traffic flow
• Vehicle speeds
• Queue lengths
• Network density
• Level of Service (LOS)
• Emissions

By forecasting these indicators before conditions deteriorate, operators gain valuable time to respond proactively.

For example, if the system predicts increasing congestion along a major corridor in the next 30 to 45 minutes, traffic managers can implement mitigation measures before queues begin to form. Similarly, areas expected to experience elevated emissions can be identified early, supporting environmental and sustainability objectives.

A major differentiator of the Landau in der Pfalz' deployment is its ability to connect insights with action.

The Digital Twin is integrated with operational traffic management strategies, allowing forecasted conditions to trigger predefined responses. Based on predicted traffic performance, operators can adjust signal timings, activate corridor strategies, or implement other network management measures.

This transforms the Digital Twin from a monitoring tool into a practical decision-support system that helps cities optimize mobility in real time. Instead of simply observing traffic conditions, operators can actively shape them.

The benefits extend beyond daily operations. The same Digital Twin can also be used to evaluate future scenarios and infrastructure changes before they are implemented in the field.

Traffic engineers can modify lane configurations, adjust speed limits, introduce temporary road closures, or test roadwork scenarios directly within the model. The system then simulates the impact of these changes using either user-defined traffic patterns or live traffic conditions. This allows decision-makers to answer critical questions before making investments or operational changes:

• What happens if a lane is closed?
• How will roadworks affect nearby intersections?
• What impact will a new traffic arrangement have on congestion and emissions?

By testing alternatives in a risk-free virtual environment, cities can make more informed decisions and avoid costly surprises.

As urban mobility becomes increasingly complex, cities need new tools to understand and manage their transportation networks holistically.

The Digital Twin deployed in Landau in der Pfalz demonstrates how existing traffic infrastructure, real-time data, AI forecasting, and simulation can be combined into a single operational platform that supports both daily traffic management and long-term planning.

Most importantly, it proves that Digital Twins are no longer theoretical concepts. They are practical, operational solutions that help cities move from reactive traffic management to proactive mobility management.