What We Do

More Effective Urban Planning

Digital twins enable planned infrastructure projects (roads, bridges, buildings) to be tested in a virtual environment. This allows potential impacts to be analyzed in advance.

Predictable Traffic Planning

By using digital twins, the urbanization process of new areas can be optimized by taking into account factors such as traffic density, need for green space, and population density.

Advanced Traffic Management

Real-time data can be used to monitor traffic flow and identify problem areas. Danger zones can be identified and preventive measures can be taken.

Signaling Management

The difference is made in smart transportation solutions such as traffic light synchronization and optimization of public transport routes.

Energy Management

Energy use of buildings and infrastructure can be monitored to increase efficiency. Carbon emissions in the city can be modeled and reduction strategies can be developed.

Infrastructure and Superstructure Management

With digital twins, you can manage infrastructure lines from a single point in a digital environment and simulate the costs of investments such as bridges and underpasses in advance.

Zoning Management

You can track areas open to development and predict all possible scenarios by simulating areas that will undergo urban transformation.

Emergency Management

Scenarios can be created for natural disasters such as earthquakes, floods, fires or storms. This helps develop effective evacuation plans

How Do IT

Real-Time Data Integration

Digital twins continuously collect data from physical assets, keeping the virtual model updated in real-time. This is achieved through sensors and other data collection tools. Real-time data is essential for monitoring processes and making quick interventions.

Accuracy and Precision

A digital twin must accurately represent the physical asset. This requires precise modeling of the system’s mathematical, physical, and behavioral characteristics. Inaccurate or incomplete data can compromise the validity of simulations and analyses.

Prediction and Simulation Capability

Digital twins should be able to simulate and predict future events and scenarios. By testing different scenarios, the system’s performance can be optimized, and potential issues can be identified in advance.

Adaptability and Learning Capacity

Digital twins must have the ability to adapt to continuously changing conditions. Supported by machine learning and artificial intelligence algorithms, a digital twin can learn from past data and make more accurate predictions in the future.

Monitoring and Diagnostic Capacity

Digital twins enable continuous monitoring of physical assets, allowing for early detection of malfunctions or performance drops. This helps reduce maintenance costs and improve operational efficiency.

Interactive and User-Friendly Interface

A digital twin should have an interface that allows users to easily interact with the data. Visualization, data analytics tools, and user-friendly control mechanisms make decision-making easier and make the digital twin more accessible.