Over the last decade, there has been an increase in air transportation, which has raised awareness to provide necessary procedures to respond to air transportation demands and reduce delay costs. One such procedure, “performance-based navigation (PBN),” currently studied, is expected to increase the airspace capacity based on aircraft's individual performances. This research describes an efficient and expandable model for allocating standard terminal arrival route during continuous descent approach (CDA) that effectively uses PBN requirements. As a case study, we have considered the “Atlanta International Airport” and verified the model's accuracy using real-time traffic data. The model is then used to study mixed traffic behavior concerning each aircraft's navigation capability. We show that our technique helps decrease time separation minima, reducing traffic delays by up to 50% during busy periods. Furthermore, we show that our approach could enhance target traffic flow by as much as 25% while maintaining proper separation minima. Finally, we show that near-optimal control advisory could also be obtained to minimize the target traffic flow's total delay.

中文翻译：

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使用元胞自动机的基于 PBN 的时间最优终端空中交通控制

在过去十年中，航空运输有所增加，这提高了提供必要程序以响应航空运输需求和减少延误成本的意识。目前正在研究的一种此类程序“基于性能的导航（PBN）”有望根据飞机的个人性能增加空域容量。本研究描述了一种有效且可扩展的模型，用于在有效使用 PBN 要求的连续下降进近 (CDA) 期间分配标准终端到达路线。作为案例研究，我们考虑了“亚特兰大国际机场”，并使用实时交通数据验证了模型的准确性。然后使用该模型来研究与每架飞机的导航能力有关的混合交通行为。我们展示了我们的技术有助于减少时间间隔最小值，在繁忙时段减少高达 50% 的交通延迟。此外，我们表明我们的方法可以将目标交通流量提高多达 25%，同时保持适当的最小间隔。最后，我们表明还可以获得接近最优的控制建议，以最小化目标交通流的总延迟。