Real-Time Go-Around Prediction: A case study of JFK airport
<|reference_start|>Real-Time Go-Around Prediction: A case study of JFK airport: 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.<|reference_end|>
@article{liu2024real-time,
    title={Real-Time Go-Around Prediction: A case study of JFK airport},
    author={Ke Liu, Kaijing Ding, Lu Dai, Mark Hansen, Kennis Chan, John Schade},
    journal={International Conference on Research in Air Transportation
  (ICRAT2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12244},
    primaryClass={physics.soc-ph cs.LG}
}