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A machine learning model to predict runway exit at Vienna airport

Author

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  • Herrema, Floris
  • Curran, Ricky
  • Hartjes, Sander
  • Ellejmi, Mohamed
  • Bancroft, Steven
  • Schultz, Michael

Abstract

Runway utilisation is a function of actual yearly runway throughput and annual capacity. The aim of the analysis in this project is to find data driven prediction models based on the features and relevant scenarios that might impact runway utilisation. The Gradient Boosting machine learning method will be assessed on their forecast performance and computational time for predicting the procedural and non-procedural runway exit to be utilised after the landing rollout. The Gradient Boosting method obtained an accuracy of 79% and was used to observe key related precursors of unique data patterns. Tests were conducted using runway and final approach data consisting of 54,679 arrival flights at Vienna airport.

Suggested Citation

  • Herrema, Floris & Curran, Ricky & Hartjes, Sander & Ellejmi, Mohamed & Bancroft, Steven & Schultz, Michael, 2019. "A machine learning model to predict runway exit at Vienna airport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 329-342.
    • Handle: RePEc:eee:transe:v:131:y:2019:i:c:p:329-342
    DOI: 10.1016/j.tre.2019.10.002
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    References listed on IDEAS

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    1. Guépet, Julien & Briant, Olivier & Gayon, Jean-Philippe & Acuna-Agost, Rodrigo, 2017. "Integration of aircraft ground movements and runway operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 131-149.
    2. Xinhua Zhu & Nan Li & Yu Sun & Hongfei Zhang & Kai Wang & Sang-Bing Tsai, 2018. "A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions," Energies, MDPI, vol. 11(9), pages 1-15, September.
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    Cited by:

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    6. Olivares, Felipe & Sun, Xiaoqian & Wandelt, Sebastian & Zanin, Massimiliano, 2023. "Measuring landing independence and interactions using statistical physics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
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    8. Halpern, Nigel & Mwesiumo, Deodat & Suau-Sanchez, Pere & Budd, Thomas & Bråthen, Svein, 2021. "Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports," Journal of Air Transport Management, Elsevier, vol. 90(C).
    9. Rott, Julian & König, Fabian & Häfke, Hannes & Schmidt, Michael & Böhm, Markus & Kratsch, Wolfgang & Krcmar, Helmut, 2023. "Process Mining for resilient airport operations: A case study of Munich Airport’s turnaround process," Journal of Air Transport Management, Elsevier, vol. 112(C).

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