Abstract
Go-arounds are a necessary aspect of commercial aviation and are conducted after a landing attempt has been aborted. It is necessary to conduct go-arounds in the safest possible manner, as go-arounds are the most safety-critical of operations. Recently, the increased availability of data, such as ADS-B, has provided the opportunity to leverage machine learning and data analytics techniques to assess aviation safety events. This paper presents a framework to detect go-around flights, identify relevant features, and utilize unsupervised clustering algorithms to categorize go-around flights, with the objective of gaining insight into aspects of typical, nominal go-arounds and factors that contribute to potentially abnormal or anomalous go-arounds. Approaches into San Francisco International Airport in 2019 were examined. A total of 890 flights that conducted a single go-around were identified by assessing an aircraft’s vertical rate, altitude, and cumulative ground track distance states during approach. For each flight, 61 features relevant to go-around incidents were identified. The HDBSCAN clustering algorithm was leveraged to identify nominal go-arounds, anomalous go-arounds, and a third cluster of flights that conducted a go-around significantly later than other go-around trajectories. Results indicate that the go-arounds detected as being anomalous tended to have higher energy states and deviations from standard procedures when compared to the nominal go-arounds during the first approach, prior to the go-around. Further, an extensive comparison of energy states between nominal flights, anomalous flights, the first approach prior to the go-around, and the second approach following the go-around is presented.
Highlights
Introduction and BackgroundA go-around is a maneuver performed by a pilot after a decision has been made to discontinue a landing attempt
Go-arounds are conducted for a variety of reasons, including unstable approach, adverse weather, degraded conditions on the runway, a request by air traffic control (ATC), etc
This paper presented a novel methodology to classify and analyze go-arounds
Summary
Introduction and BackgroundA go-around is a maneuver performed by a pilot after a decision has been made to discontinue a landing attempt. Go-arounds are conducted for a variety of reasons, including unstable approach, adverse weather, degraded conditions on the runway, a request by air traffic control (ATC), etc. An unstable approach is said to occur when an aircraft does not maintain either its speed, descent rate, glide slope, or localizer on approach [1]. While a single short-haul commercial airline pilot may only conduct a go-around at a rate of once or twice per year, cumulatively, go-arounds occur at an average rate of one to three per 1000 commercial flight approaches [2]. Due to the frequency of go-arounds, they may be considered a relatively significant event in commercial aviation operations. Go-arounds are an important event to investigate to contribute to improving overall aviation safety
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