IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v131y2019icp329-342.html
   My bibliography  Save this article

A machine learning model to predict runway exit at Vienna airport

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554518313383
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2019.10.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Schultz, Michael & Rosenow, Judith & Olive, Xavier, 2022. "Data-driven airport management enabled by operational milestones derived from ADS-B messages," Journal of Air Transport Management, Elsevier, vol. 99(C).
    2. Truong, Dothang, 2021. "Using causal machine learning for predicting the risk of flight delays in air transportation," Journal of Air Transport Management, Elsevier, vol. 91(C).
    3. 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).
    4. 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).
    5. Ding, Yida & Wandelt, Sebastian & Wu, Guohua & Xu, Yifan & Sun, Xiaoqian, 2023. "Towards efficient airline disruption recovery with reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    6. Schultz, Michael & Soolaki, Majid & Salari, Mostafa & Bakhshian, Elnaz, 2023. "A combined optimization–simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities," Journal of Air Transport Management, Elsevier, vol. 106(C).
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Yu & Xue, Qingwen & Wang, Yasha & Cai, Mengting & Zhang, Honghai & Li, Yahui, 2021. "Traffic congestion mechanism in mega-airport surface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    2. Dönmez, Kadir & Çetek, Cem & Kaya, Onur, 2022. "Air traffic management in parallel-point merge systems under wind uncertainties," Journal of Air Transport Management, Elsevier, vol. 104(C).
    3. Serhan, Duaa & Lee, Hanbong & Yoon, Sang Won, 2018. "Minimizing airline and passenger delay cost in airport surface and terminal airspace operations," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 120-133.
    4. Bojan Kranjec & Sasa Sladic & Wojciech Giernacki & Neven Bulic, 2018. "PV System Design and Flight Efficiency Considerations for Fixed-Wing Radio-Controlled Aircraft—A Case Study," Energies, MDPI, vol. 11(10), pages 1-12, October.
    5. Nan Li & Yu Sun & Jian Yu & Jian-Cheng Li & Hong-fei Zhang & Sangbing Tsai, 2019. "An Empirical Study on Low Emission Taxiing Path Optimization of Aircrafts on Airport Surfaces from the Perspective of Reducing Carbon Emissions," Energies, MDPI, vol. 12(9), pages 1-19, April.
    6. Wen-Hsien Tsai, 2019. "Modeling and Simulation of Carbon Emission-Related Issues," Energies, MDPI, vol. 12(13), pages 1-8, July.
    7. Li, Max Z. & Ryerson, Megan S. & Balakrishnan, Hamsa, 2019. "Topological data analysis for aviation applications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 149-174.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:131:y:2019:i:c:p:329-342. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.