Skip to main content

Research Repository

Advanced Search

Aircraft taxi time prediction: comparisons and insights

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


Stefan Ravizza

Jun Chen

Jason A.D. Atkin

Paul Stewart

Edmund K. Burke


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.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal Applied Soft Computing
Print ISSN 1568-4946
Electronic ISSN 1872-9681
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 14
Issue C
APA6 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),
Keywords Data mining; Fuzzy rule-based system; Regression; Airport ground movement; Decision support system
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:


_ASC_Ravizza_new.pdf (390 Kb)

Copyright Statement
Copyright information regarding this work can be found at the following address:

Downloadable Citations