Sequential Prediction of Go-Around Occurrence

Lu Dai, Yulin Liu and Mark Hansen

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

Abstract: A go-around is an aborted landing event of an aircraft that is on final approach. Go-arounds are costly and detrimental to safety. Building upon our previous work in go-around detection and analysis of feature contributions, we investigate different learning models and prediction regimes for making sequential predictions of go-around probabilities based on realized trajectory data and environment factors as the aircraft proceeds on its approach. This paper develops and compares the performance of different learning algorithms and prediction strategies for the sequential go-around prediction problem. Applying these methods to a data set consisting of more than 100,000 flight approaches into JFK airport, we find that the Input Output Hidden Markov Model with multi-step prediction strategy, in general, outperforms other models due to its capability of capturing the inherent temporal structure of the entire flight sequence.

Keywords: Engineering; Go-around; Hidden Markov model; Machine learning; Sequence Classification; Multi-step prediction (search for similar items in EconPapers)
Date: 2022-01-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.escholarship.org/uc/item/0s50w7rg.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt0s50w7rg

Access Statistics for this paper

More papers in Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().