Real-Time Go-Around Prediction: A case study of JFK airport
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.
Cite
@article{arxiv.2405.12244,
title = {Real-Time Go-Around Prediction: A case study of JFK airport},
author = {Ke Liu and Kaijing Ding and Lu Dai and Mark Hansen and Kennis Chan and John Schade},
journal= {arXiv preprint arXiv:2405.12244},
year = {2024}
}
Comments
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