Please use this identifier to cite or link to this item: http://hdl.handle.net/11667/114
Appears in Collections:University of Stirling Research Data
Title: Data for the paper "A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation"
Creator(s): Brownlee, Alexander E I
Weiszer, Michael
Chen, Jun
Ravizza, Stefan
Woodward, John R
Burke, Edmund K
Contact Email: sbr@cs.stir.ac.uk
Keywords: Routing
Scheduling
Airport Operations
Optimization
Taxiing
Ground Movement
Uncertainty
Date Available: 1-May-2018
Citation: Brownlee, AEI; Weiszer, M; Chen, J; Ravizza, S; Woodward, JR, Burke, EK (2018): Data for the paper "A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation". University of Stirling. Dataset. http://hdl.handle.net/11667/114
Dataset Description (Abstract): Allocating efficient routes to taxiing aircraft, known as the Ground Movement problem, is increasingly important as air traffic levels continue to increase. If taxiways cannot be reliably traversed quickly, aircraft can miss valuable assigned slots at the runway or can waste fuel waiting for other aircraft to clear. Efficient algorithms for this problem have been proposed, but little work has considered the uncertainties inherent in the domain. This paper proposes an adaptive Mamdani fuzzy rule based system to estimate taxi times and their uncertainties. Furthermore, the existing Quickest Path Problem with Time Windows (QPPTW) algorithm is adapted to use fuzzy taxi time estimates. Experiments with simulated taxi movements at Manchester Airport, the third-busiest in the UK, show the new approach produces routes that are more robust, reducing delays due to uncertain taxi times by 10-20% over the original QPPTW. The raw aircraft movement data cannot be shared due to licensing restrictions: this set contains the speed data, traffic and layout scenarios, and experimental results.
Dataset Description (TOC): Data for the paper "A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation in 1 file folder called: ForDataRepo.zip. Dedicated UnZip software is recommended for accessing the dataset, for example, IZArc.
Type: dataset
Contract/Grant Title: SANDPIT: Integrating and Automating Airport Operations
DAASE: Dynamic Adaptive Automated Software Engineering
TRANSIT: Towards a Robust Airport Decision Support System
Funder(s): EPSRC - Engineering and Physical Sciences Research Council
Contract/Grant Number: EP/H004424/2
EP/J017515/1
EP/N029496/2
EP/N029577/1
Geographic Location(s): Manchester Airport
URI: http://hdl.handle.net/11667/114
Rights: Rights covered by the standard CC-BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/
Affiliation(s) of Dataset Creator(s): University of Stirling (Computing Science - CSM Dept)
Queen Mary University of London
IBM Global Business Services

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