digital twin Cologne-Bonn Airport

12 December 2025

A digital airport twin is more than an updated digital representation of the physical airport. It constitutes a virtual environment in which all essential aspects are represented and operational processes are modeled and simulated. The key aspect is that this virtual environment is continuously updated with real-world data, thereby synchronizing all internal system states. Planned operations can thus be forecast based on the most recent data, while missing information within the model is substituted by virtual data and complemented through data-driven analyses.

The main focus areas of student projects include the preparation and enhancement of infrastructure data, the implementation of simulation components (e.g. turnaround processes, apron movements), as well as the development of visualization components. Depending on individual skills and interests, additional aspects may also be addressed within student projects.

  • Data and Integration
    • Connection and integration of additional real-time data sources (e.g. weather data, ADS-B, IoT sensors, ground handling systems)
    • Data harmonization and quality assurance of heterogeneous data sources
    • Development of interfaces (APIs) between existing airport IT systems and the digital twin
    • Handling of missing, uncertain, or delayed data (imputation methods, uncertainty models)

 

  • Simulation and Modeling
    • Modeling of additional operational processes (e.g. de-icing, crew changes, baggage logistics, refueling)
    • Multi-agent simulation of vehicles, personnel, and aircraft
    • Representation of disruptions and exceptional events (delays, equipment failures, weather events)
    • Scaling of the simulation for different airport sizes or traffic scenarios

 

  • Forecasting and Optimization
    • Development of forecasting models for process durations, delays, or resource demands
    • Application of machine learning methods for pattern recognition in operational data
    • Optimization of turnaround times, gate assignments, or apron routing
    • Scenario analysis and what-if simulations for operational decision support

 

  • Visualization and Interaction
    • Development of interactive 2D/3D visualizations (dashboards, heatmaps, timelines)
    • Enhancement of 3D representations with semantic layers (status, utilization, risks)
    • VR/MR-based visualization and interaction with the digital twin
    • Development of role-based views (operations, management, training)

 

  • Human–System Interaction
    • Design and evaluation of user interfaces and decision support tools
    • Integration of human-in-the-loop approaches
    • Investigation of situational awareness and cognitive workload
    • Development of training and educational scenarios

 

  • Validation and Evaluation
    • Validation of simulation models using real operational data
    • Development of KPIs for assessing process quality and robustness
    • Comparison of real-world and simulated operations
    • Sensitivity and robustness analyses

 

  • IT Architecture and Operation
    • Design of scalable software architectures for digital twins
    • Performance optimization (real-time capability, parallelization)
    • Version and scenario management
    • Consideration of IT security, data protection, and access control

Aktuelles

digital cabin

Design of the experimental setup and conduct of experiments in a digital aircraft cabin

urban air mobility simulator

Enhancement of a simulation environment for urban air traffic under potential emergency conditions

Implications of runway direction changes

This paper presents a runway-aware approach for Singapore Changi Airport that adjusts aircraft trajectories based on dynamic runway capacity, reducing arrival overloads by 81% through modest upstream time shifts without aggressive tactical intervention.

Delivery route optimization for network efficiency

This paper presents a two-stage optimization method for urban drone route networks that balances delivery efficiency, airspace deconfliction, and noise exposure, validated through a real-world medical delivery case study in Paris.

Mitigate air traffic complexity in moving sectors

Moving sectors enable flow-centric management with enhanced airspace capacity, as controller workload is dynamically allocated according to traffic conditions.

Range-angle likelihood maps for indoor positioning

We develop a ResNet-based deep learning approach for indoor aircraft cabin positioning, achieving centimeter-level localization accuracy by transforming range and angle measurements into likelihood grid maps and optimizing model hyperparameters.