IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v86y2020ics0969699719305575.html
   My bibliography  Save this article

Using machine learning algorithms to predict the risk of small Unmanned Aircraft System violations in the National Airspace System

Author

Listed:
  • Truong, Dothang
  • Choi, Woojin

Abstract

The increasing number of small Unmanned Aircraft System (sUAS) encounters with manned aircraft or airports increases the risk of collision in the National Airspace System. The purpose of this research is to develop and test predictive models for sUAS violation incidents in NAS using machine learning. This research uses machine learning algorithms to predict the risk of sUAS violation incidents using the FAA's UAS sighting data with a sample size of 2088. Three sUAS violation types are identified: flying above 400 feet, flying with 5 miles from an airport, and flying in restricted airspace. Seven machine learning algorithms were used, including classification regression, decision tree, neural network, gradient boosting, random forest, Bayesian networks, and Memory-Based Reasoning. The results show that Gradient boosting produces the best predictive model. This model can predict the sUAS violation incidents with an accuracy of 95.7 percent. Location, distance to the airport, state, sUAs altitude, airport type, and aircraft type are the most influential predictors to the sUAS violation incidents.

Suggested Citation

  • Truong, Dothang & Choi, Woojin, 2020. "Using machine learning algorithms to predict the risk of small Unmanned Aircraft System violations in the National Airspace System," Journal of Air Transport Management, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:jaitra:v:86:y:2020:i:c:s0969699719305575
    DOI: 10.1016/j.jairtraman.2020.101822
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699719305575
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jairtraman.2020.101822?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. Washington, Achim & Clothier, Reece A. & Williams, Brendan P., 2017. "A Bayesian approach to system safety assessment and compliance assessment for Unmanned Aircraft Systems," Journal of Air Transport Management, Elsevier, vol. 62(C), pages 18-33.
    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. Truong, Dothang, 2021. "Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation," Journal of Air Transport Management, Elsevier, vol. 96(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. Wallace, Ryan J. & Winter, Scott R. & Rice, Stephen & Loffi, Jon M. & Misra, Shlok & Lee, Sang-A & Park, Joshua & Neff, Abigail & McNall, Cole, 2024. "An examination of sUAS operations in proximity to a major U.S. Airport," Technology in Society, Elsevier, vol. 76(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.

      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:jaitra:v:86:y:2020:i:c:s0969699719305575. 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.journals.elsevier.com/journal-of-air-transport-management/ .

      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.