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对复飞的可预测性统计

On the Statistics and Predictability of Go-Arounds
课程网址: http://videolectures.net/cidu2011_gariel_go_arounds/  
主讲教师: Maxime Gariel
开课单位: 罗克韦尔·柯林斯公司
开课时间: 2012-06-27
课程语种: 英语
中文简介:
本文采用实证方法来确定繁忙机场的运营因素, 这些因素可能会发生在绕行前。利用旧金山国际机场四年的数据, 我们首先分析了可能增加绕行发生概率的着陆飞机序列。然后, 我们采取统计方法, 调查机载、地面作业 (例如入境飞机数量、从登机口滑行的飞机数量等) 或天气在分钟内最可能波动的特征就在错过的方法之前。我们分析了这些发现, 包括它们对当前机场运营的影响, 并讨论了前置因素可能如何影响 nextgen。最后, 作为协助空中交通管制人员的一种手段, 我们利用机器学习社区的技术, 开发了一个初步的预警系统, 用于绕行预测。
课程简介: 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 an analysis of sequence of landing aircraft that may increase the probability of go-around occurrence. Then we take 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.
关 键 词: 计算机科学; 数据挖掘; 统计和共识的方法
课程来源: 视频讲座网
最后编审: 2020-07-06:heyf
阅读次数: 34