Aircraft taxi time prediction: comparisons and insights

Ravizza, Stefan; Chen, Jun; Atkin, Jason A.D.; Stewart, Paul; Burke, Edmund K.

Abstract

The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 min makes efficient and predictable ground operations at airports indispensable. Accurately predicting taxi times of arrivals and departures serves as an important key task for runway sequencing, gate assignment and ground movement itself. This research tests different statistical regression approaches and also various regression methods which fall into the realm of soft computing to more accurately predict taxi times. Historic data from two major European airports is utilised for cross-validation. Detailed comparisons show that a TSK fuzzy rule-based system outperformed the other approaches in terms of prediction accuracy. Insights from this approach are then presented, focusing on the analysis of taxi-in times, which is rarely discussed in literature. The aim of this research is to unleash the power of soft computing methods, in particular fuzzy rule-based systems, for taxi time prediction problems. Moreover, we aim to show that, although these methods have only been recently applied to airport problems, they present promising and potential features for such problems.

Citation

Ravizza, S., Chen, J., Atkin, J. A., Stewart, P., & Burke, E. K. (2014). Aircraft taxi time prediction: comparisons and insights. Applied Soft Computing, 14(C), https://doi.org/10.1016/j.asoc.2013.10.004

• Journal Article Type: Article
• Acceptance Date: Oct 6, 2013
• Online Publication Date: Oct 23, 2013
• Publication Date: Jan 1, 2014
• Deposit Date: Oct 19, 2016
• Publicly Available Date: Oct 19, 2016
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: C
• DOI: https://doi.org/10.1016/j.asoc.2013.10.004
• Keywords: Data mining; Fuzzy rule-based system; Regression; Airport ground movement; Decision support system
• Public URL: https://nottingham-repository.worktribe.com/output/997624
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1568494613003384
• Contract Date: Oct 19, 2016