Real-Time Go-Around Prediction: A case study of JFK airport
This work addresses safety and efficiency for aviation personnel by providing a real-time prediction tool, though it is incremental as it applies an existing method to a specific airport case.
The paper tackled real-time go-around prediction for arrival flights at JFK airport using an LSTM model, identifying in-trail spacing and simultaneous runway operations as top contributing factors, and developed a web-based tool for flight crews to assess high-risk situations.
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.