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• W222856343 abstract "The gradual introduction of advanced ATM procedures such as Continuous Descent Approaches (CDA) creates a challenge when balancing the capacity-demand of arrival traffic in the presence of constrained ground (runway, taxiway, gate) resources. Part of the challenge is to understand the interdependency between spatial-temporal distribution of arrival traffic (traffic distribution) and the dynamics of ground resources to better manage sustain and improve the airport throughput capacity and to minimize delays. In this paper, we use the Computational Red Teaming (CRT) Framework to identify patterns in arrival traffic and ground events that lead to delays in dynamic CDA scenarios. The scenarios represent the interaction of ground events with traffic distributions. The search engine in CRT relies on co-evolutionary search, with the reciprocal interaction of traffic distributions and ground events evolving to identify bottlenecks in the system. With each interaction various metrics are recorded and are then data-mined to identify patterns that lead to delays. Results identified scenarios whereby delays become seriously significant. For example, for a model of the Sydney domestic terminal area in a dynamic CDA scenario, flights arriving from the South-East direction with an average inter-arrival time of 53 sec can cause significant delays if runway 16L is impacted by a ground event. Another example identified taxiway C as a critical ground resource for arrival throughput capacity." @default.
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• W222856343 date "2012-01-01" @default.
• W222856343 modified "2023-09-27" @default.
• W222856343 title "Discovering Delay Patterns in Arrival Traffic with Dynamic Continuous Descent Approaches Using Co-Evolutionary Red Teaming" @default.
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• W222856343 doi "https://doi.org/10.2514/atcq.20.1.47" @default.
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