Applying digital twins for the management of information in turnaround event operations in commercial airports
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Abstract (arXiv)
The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation's and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Aug 2024.
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2025Real-time Spatial Retrieval Augmented Generation for Urban Environmentscited by 1
- 2024Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systemscited by 0
- 2025PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Modelscited by 0
- 2025Real-time Spatial Retrieval Augmented Generation for Urban Environments4 May 2025
- 2025PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models25 Apr 2025
- 2024Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems16 Nov 2024