In this paper, we employ the long-short-term memory model (LSTM) to predict
the real-time go-around probability as an arrival flight is approaching JFK
airport and within 10 nm of the landing runway threshold. We further develop
methods to examine the causes to go-around occurrences both from a global view
and an individual flight perspective. According to our results, in-trail
spacing, and simultaneous runway operation appear to be the top factors that
contribute to overall go-around occurrences. We then integrate these
pre-trained models and analyses with real-time data streaming, and finally
develop a demo web-based user interface that integrates the different
components designed previously into a real-time tool that can eventually be
used by flight crews and other line personnel to identify situations in which
there is a high risk of a go-around.

我们利用长短期记忆模型 (LSTM) 预测以实时方式到达 JFK 机场并在距离着陆跑道阈值 10 纳米内的航班中，可能执行盘旋操作的概率。我们进一步开发方法来从全局视角和个别飞行视角检查引起盘旋操作的原因。根据我们的结果，紧随其后的间隔和同时运行跑道似乎是导致总体盘旋操作的主要因素。然后，我们将这些预先训练的模型和分析与实时数据流集成，并最终开发了一个演示基于 Web 的用户界面，将之前设计的不同组件整合成一个可供飞行人员和其他线路人员使用的实时工具，用于识别可能发生盘旋操作的高风险情况。