Robust regression and outlier detection
Robust regression and outlier detection
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Inference for the Generalization Error
Machine Learning
A tutorial on support vector regression
Statistics and Computing
Hybrid Metaheuristics to Aid Runway Scheduling at London Heathrow Airport
Transportation Science
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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The predicted growth in air transportation and the ambitious goal of the European Commission to have on-time performance of flights within 1min 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.