On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports

Authors Jun Chen, Stefan Ravizza, Jason A. D. Atkin, Paul Stewart

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Jun Chen
Stefan Ravizza
Jason A. D. Atkin
Paul Stewart

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Jun Chen, Stefan Ravizza, Jason A. D. Atkin, and Paul Stewart. On the Utilisation of Fuzzy Rule-Based Systems for Taxi Time Estimations at Airports. In 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Open Access Series in Informatics (OASIcs), Volume 20, pp. 134-145, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)


The primary objective of this paper is to introduce Fuzzy Rule-Based Systems (FRBSs) as a relatively new technology into airport transportation research, with a special emphasis on ground movement operations. Hence, a Mamdani FRBS with the capability to learn from data has been adopted for taxi time estimations at Zurich Airport (ZRH). Linear regression is currently the dominating technique for such an estimation task due to its established nature, proven mathematical characteristics and straightforward explanatory ability. In this study, we demonstrate that FRBSs, although having a more complex structure, can offer more accurate estimations due to their proven properties as nonlinear universal approximators. Furthermore, such improvements in accuracy do not come at the cost of the model's interpretability. FRBSs can offer more explanations of the underlying behavior in different regions. Preliminary results on data for ZRH suggest that FRBSs are a valuable alternative to already established linear regression methods. FRBSs have great potential to be further seamlessly integrated into the taxiway routing and scheduling process due to the fact that more information is now available in the explanatory variable space.
  • Fuzzy rule-based system
  • Taxi time estimation
  • Airport ground movement


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