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Abstract
Future air traffic management (ATM) systems are expected to handle the increasingly heavy demand on air traffic, especially in the highly constrained terminal manoeuvring area (TMA). However, the realizable capacity of current TMA is a challenge for future air transportation development. This is due to the limitations in accommodating safe and efficient travel under the highly limited airspace configuration strategies and pre-defined terminal trajectories. Therefore, making the TMA resources flexible and available corresponding to different traffic scenarios is the key to enhance the practical ATM efficiency in future TMAs.
Improving the TMA airspace configuration to balance capacity and demand is a challenging task, since a TMA system inherently involves high uncertainties and multiple interactions among many different components. The inherent complexity of the TMA necessitates a system-level analysis approach, in which each component is investigated through modelling the complex interactions among other parts of the environment in which it operates. Hence, the process of understanding (through modelling), evaluating and dynamically designing TMA airspace configurations, while considering dynamic constrained ground resources, is becoming crucial for enhancing the practical ATM efficiency in future TMAs.
A simulation-based co-evolutionary computational environment -- Co-evolutionary Computational Red Teaming (CCRT) -- is developed for evaluating advanced TMA airspace concepts and understanding the TMA system-level vulnerabilities. A novel TMA airspace design concept for capacity-demand balancing including a measure of collision risks derived from the probabilistic nature of aircraft's performance is proposed. A multi-objective CCRT is proposed to generate scenario-specific TMA airspace design strategies that are able to cope better with ground events/uncertainties and produce dynamic trajectories while maintaining ATM efficiency and aircraft safety. The multi-objective CCRT also provides an analyst with the trade-off between these two air traffic control priorities - efficiency and safety; thus solutions can be selected based on the criticality level of meeting the demand.