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

TL;DR

This paper presents a real-time go-around prediction system at JFK airport using LSTM models, analyzing causes and providing a web-based tool for flight crews to assess go-around risks.

Contribution

It introduces a novel real-time prediction framework combining LSTM models with cause analysis and a user interface for operational use.

Findings

In-trail spacing significantly affects go-around likelihood.

Simultaneous runway operations increase go-around risk.

The system effectively predicts go-arounds in real-time.

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.