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Ravizza, Stefan (2013)
Languages: English
Types: Unknown
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputerApplications_COMPUTERSINOTHERSYSTEMS
With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. The ground movement problem forms the link between other airside problems at an airport, such as arrival sequencing, departure sequencing, gate/stand allocation and stand holding. The purpose of this thesis is to contribute to airport ground movement research through obtaining a better understanding of the problem and producing new models and algorithms for three sub-problems. Firstly, many stakeholders at an airport can benefit from more accurate taxi time predictions. This thesis focuses upon this aim by analysing the important factors affecting taxi times for arrivals and departures and by comparing different regression models to analyse which one performs the best for this particular task. It was found that incorporating the information of the airport layout could significantly improve the accuracy and that a TSK fuzzy rule-based system outperformed other approaches. Secondly, a fast and flexible decision support system is introduced which can help ground controllers in an airport tower to make better routing and scheduling decisions and can also absorb as much of the waiting time as possible for departures at the gate/stand, to reduce the fuel burn and environmental impact. The results show potential maximum savings in total taxi time of about 30.3%, compared to the actual performance at the airport. Thirdly, a new research direction is explored which analyses the trade-off between taxi time and fuel consumption during taxiing. A sophisticated new model is presented to make such an analysis possible. Furthermore, this research provides the basis for integrating the ground movement problem with other airport operations. Datasets from Zurich Airport, Stockholm-Arlanda Airport, London Heathrow Airport and Hartsfield-Jackson Atlanta International Airport were utilised to test these sub-problems.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 2.1 Di erent routes at Zurich Airport . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
    • 3.1 Sketch of Zurich Airport (ZRH) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
    • 3.2 Layout of Zurich Airport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
    • 3.3 Hours of the day at ZRH (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
    • 3.4 Hours of the day at ZRH (2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
    • 3.5 Sketch of Stockholm-Arlanda Airport (ARN) . . . . . . . . . . . . . . . . . . . . 42
    • 3.6 Layout of Stockholm-Arlanda Airport . . . . . . . . . . . . . . . . . . . . . . . . 43
    • 3.7 Hours of the day at ARN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
    • 3.8 Layout of London Heathrow Airport (LHR) . . . . . . . . . . . . . . . . . . . . . 45
    • 4.1 Average speed at ARN from two di erent stand groups . . . . . . . . . . . . . . 52
    • 4.2 Scatterplots showing the logarithmic transformation . . . . . . . . . . . . . . . . 56
    • 4.3 Measuring turning angle of aircraft on one vertex . . . . . . . . . . . . . . . . . . 57
    • 4.4 Scatterplot showing the linear t for ARN . . . . . . . . . . . . . . . . . . . . . . 61
    • 4.5 Scatterplot showing the linear t for ZRH . . . . . . . . . . . . . . . . . . . . . . 62
    • 4.6 Residual plots showing the validation of the assumptions . . . . . . . . . . . . . . 64
    • 4.7 Normal Q-Q-plots showing the validation of the assumptions . . . . . . . . . . . 64
    • 4.8 Taxi time prediction accuracy at ARN . . . . . . . . . . . . . . . . . . . . . . . . 70
    • 4.9 Scatterplots showing the linear t for LHR . . . . . . . . . . . . . . . . . . . . . 72
    • 5.1 E ect of an outlier on least median squared and least squared regression . . . . . 80
    • 5.2 Linear support vector regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
    • 5.3 Example of M5 model tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
    • 5.4 Fuzzy Inference Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
    • 5.5 The shape of a Gaussian membership function for the explanatory variables . . . 83
    • 5.6 Taxi time prediction accuracy at ZRH . . . . . . . . . . . . . . . . . . . . . . . . 90
    • 5.7 Four fuzzy rules extracted from the TSK FRBS analysis for ARN . . . . . . . . . 92
    • 5.8 Non-linearity of TSK FRBS models shown on data from ARN . . . . . . . . . . . 94
    • 6.1 Flow chart of general concept of the approach . . . . . . . . . . . . . . . . . . . . 104
    • 6.2 Euclidian distance between two segments . . . . . . . . . . . . . . . . . . . . . . 106
    • 6.3 Sorted delay for each aircraft from the di erent heuristics . . . . . . . . . . . . . 121
    • 6.4 Sorted delay for each aircraft with and without swap heuristic . . . . . . . . . . . 122
    • 7.1 Di erent routes from pier A to runway 28 at ZRH . . . . . . . . . . . . . . . . . 133
    • 7.2 Pareto-front of unimpeded taxi trajectories . . . . . . . . . . . . . . . . . . . . . 136
    • 7.3 Combined Pareto-front from four di erent routes . . . . . . . . . . . . . . . . . . 139
    • 7.4 Global discretised Pareto-front for 57 aircraft . . . . . . . . . . . . . . . . . . . . 143
    • 3.1 Speci cations of aircraft and engines . . . . . . . . . . . . . . . . . . . . . . . . . 46
    • 4.1 Coe cients for ARN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
    • 4.2 Coe cients for ZRH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
    • 4.3 Comparison of prediction accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . 69
    • 5.1 Overview of datasets from ARN and ZRH . . . . . . . . . . . . . . . . . . . . . . 79
    • 5.2 Comparisons of performance measures for ARN and ZRH . . . . . . . . . . . . . 91
    • 5.3 Comparisons of accuracies for ARN and ZRH . . . . . . . . . . . . . . . . . . . . 92
    • 5.4 Consequence part of the TSK FRBS analysis for ARN . . . . . . . . . . . . . . . 93
    • 5.5 Consequence part of the TSK FRBS analysis for ZRH . . . . . . . . . . . . . . . 97
    • 6.1 Table of de nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
    • 6.2 Summary of the results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
    • 6.3 Analysis of ordering heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
    • 6.4 Analysis of routing and scheduling algorithm with and without swap heuristic . . 123
    • 6.5 Percentage of aircraft with more than a certain amount of delay . . . . . . . . . . 124
    • 6.6 Analysis with arti cially more ground tra c . . . . . . . . . . . . . . . . . . . . . 125
    • 7.1 Analysis over a week's operations . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
    • 7.2 Analysis with di erent fuel-related objective function . . . . . . . . . . . . . . . . 144
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    • 10 minutes
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