Aircraft taxi time prediction: comparisons and insights

Ravizza, Stefan and Chen, Jun and Atkin, Jason A. D. and Stewart, Paul and Burke, Edmund K. (2014) Aircraft taxi time prediction: comparisons and insights. Applied Soft Computing, 14 (c). pp. 397-406. ISSN 1568-4946

Full content URL: http://dx.doi.org/10.1016/j.asoc.2013.10.004

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

Abstract

The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1 minute, 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 regression approaches 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.

Keywords:Data mining, Fuzzy rule-based systems, Regression, OR in airlines, Airport ground movement, Decision support systems, NotOAChecked
Subjects:H Engineering > H230 Transport Engineering
G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G200 Operational Research
H Engineering > H400 Aerospace Engineering
Divisions:College of Science > School of Engineering
ID Code:12084
Deposited On:07 Oct 2013 07:45

Repository Staff Only: item control page