Real-Time Go-Around Prediction: A case study of JFK airport

• URL: http://arxiv.org/abs/2405.12244v1
• Date: Sat, 18 May 2024 07:39:45 GMT
• Title: Real-Time Go-Around Prediction: A case study of JFK airport
• Authors: Ke Liu, Kaijing Ding, Lu Dai, Mark Hansen, Kennis Chan, John Schade,
• Abstract summary: 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. 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.
• Score: 4.1374497211515
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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