Project Summary
This project will develop a UK Airspace Digital Twin to create a complete UK airspace traffic dataset, enabling evaluation of how many advanced air mobility aircraft can be safely managed alongside conventional traffic. By combining live crewed and uncrewed traffic data, simulated drone and eVTOL (electric Vertical Take-Off and Landing vehicle) operations, and AI-based conflict resolution, the model will deliver evidence-based insights into safe capacity limits. The results will equip policymakers, industry and regulators with robust evidence to enable the safe and scalable adoption of integrated air mobility in UK airspace.
Project Achievements
The project delivered the first integrated UK airspace digital‑twin environment that combines real‑world traffic data with adaptive AI‑based conflict resolution. We developed a complete pipeline for data ingestion, fusion, mixed‑traffic simulation, and intelligent deconfliction, resulting in a robust platform for assessing future Advanced Air Mobility (AAM) operations. Our simulations showed clear improvements in both safety and efficiency, demonstrating that AI‑enabled conflict resolution can significantly enhance the performance of mixed airspace while maintaining required safety levels. The project also created new high‑frequency datasets, an advanced digital‑twin architecture, and academic outputs that strengthen national capability in digital‑first approaches to AAM. Alongside these technical achievements, the work has helped advance understanding of how AI, digital twins, and real‑world data can support the safe and sustainable introduction of future air‑mobility services. The outcomes position the project as a meaningful step toward scalable, environmentally responsible AAM integration in the UK.
Conclusions
This project has demonstrated a robust, evidence‑driven approach to understanding how Advanced Air Mobility can be safely and efficiently integrated into UK airspace. By combining real‑world traffic data, digital‑twin technologies, and adaptive AI‑based conflict resolution, we have created a high‑fidelity environment that offers meaningful insights into future airspace operations. The results highlight the potential for intelligent services to enhance safety, improve efficiency, and support environmentally responsible planning without reliance on real‑world trial‑and‑error.
Next Steps
The next stage of this work will focus on advancing the digital‑twin framework toward operational readiness through hardware‑in‑the‑loop testing, interoperability trials, and further validation with real‑time data streams. We will integrate the platform with other UTM service providers and expand our research into persistent, AI‑enabled digital‑twin operations through upcoming projects. Continued collaboration with academic partners will support multimodal transport research and prepare for a Horizon Europe submission. These activities will strengthen technical maturity, address regulatory and data‑access challenges, and position the system for future live‑environment demonstrations and commercial pathways.
