关于复飞的统计与可预测性
On the Statistics and Predictability of Go-Arounds
AI总结：
本文通过统计分析和机器学习方法，研究繁忙机场复飞操作的前兆因素，并开发初步的复飞预警系统。
摘要：
本文采用实证方法识别繁忙机场可能预示复飞操作的操作因素。利用旧金山国际机场四年的数据，我们首先通过统计方法调查空中、地面操作（如进场飞机数量、从登机口滑行的飞机数量等）或天气的哪些特征最可能在复飞前几分钟内相对于正常操作发生波动。我们分析了这些发现对当前机场运营的影响，并讨论了前因因素可能如何影响NextGen。最后，为了协助空中交通管制员，我们利用机器学习社区的技术开发了一个初步的复飞预测预警系统。
英文摘要：
This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
