Collaborative and Intelligent Air Traffic Management Systems – Advances in AI-Driven Multi-Scale Optimisation and Towards Human–AI Collaborative Decision-Making

Abstract

The rapid growth of global air traffic, together with increasingly complex operational environments and uncertainties, is pushing today’s Air Traffic Management (ATM) systems to their performance limits. This seminar presents a trajectory-based, full-cycle and multi-scale operational framework for collaborative and intelligent ATM, driven by advanced AI and optimisation methodologies. The talk focuses on three fundamental scientific challenges that arise in large-scale, high-density airspace under layered, collaborative ATM operations:

• high-dimensional modelling and efficient optimisation for airspace demand-capacity balancing,
• spatio-temporal modelling and explainable optimisation for traffic flow stabilisation, and
• dynamic modelling and robust optimisation for inter-aircraft safe-separation maintenance.

These methods have been evaluated through high-fidelity simulation studies conducted under major research projects from EU SESAR, the Civil Aviation Authority of Singapore, and the Civil Aviation Administration of China.

Building upon these foundations, the seminar will outline a forward-looking research vision towards integrating intelligent ATM algorithms into today’s human-centred operational systems, paving the way for trusted and deployable intelligent decision-support. Together, these contributions aim to support the next generation of ATM system, making them safer, more efficient and more scalable in increasingly complex airspace environments.

Speaker

Dr Yutong Chen is a Research Fellow at the Air Traffic Management Research Institute (ATMRI) of Nanyang Technological University, Singapore. He holds two independently completed PhD degrees: one in Aerospace from Cranfield University in the UK and another in Transportation from Nanjing University of Aeronautics and Astronautics in China. His research focuses on the application of artificial intelligence to Air Traffic Management (ATM) involving both crewed and uncrewed operations. His main research topics include demand and capacity balancing, conflict detection and resolution, trajectory planning, multi-agent reinforcement learning, and human–AI teaming in ATM. Dr Chen has published widely in leading journals, including seven first-author papers in Transportation Research Part series. His work contributes to several major programmes in Europe, Singapore and China, such as the SESAR HYPERSOLVER project, Singapore’s Future Airspace Architecture, and China’s Southwest ATMB terminal airspace management project. He is the recipient of the 2024 SESAR Young Scientist Award. His research supports ongoing efforts to modernise and enhance the safety and efficiency of next-generation ATM systems.