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Data Analytics for Trajectory Selection and Preference-Model Extrapolation in the European Airspace

In: Operations Research Proceedings 2018

Author

Listed:
  • Carlo Lancia

    (Mathematical Institute Leiden University)

  • Luigi De Giovanni

    (Università degli studi di Padova)

  • Guglielmo Lulli

    (Lancaster University Management School)

Abstract

Representing airspace users’ preferences in Air Traffic Flow Management (ATFM) mathematical models is becoming of high relevance. ATFM models aim to reduce congestion (en-route and at both departure and destination airports) and maximize the Air Traffic Management (ATM) system efficiency by determining the best trajectory for each aircraft. In this framework, the a-priori selection of possible alternative trajectories for each flight plays a crucial role. In this work, we analyze initial trajectories queried from Eurocontrol DDR2 data source. Clustering trajectories yields groups that are homogeneous with respect to known (geometry of the trajectory, speed) and partially known or unknown factors (en-route charges, fuel consumption, weather, etc.). Associations between grouped trajectories and potential choice-determinants are successively explored and evaluated, and the predictive value of the determinants is finally validated. For a given origin-destination pair, this ultimately leads to determining a set of flight trajectories and information on related airspace users’ preferences.

Suggested Citation

  • Carlo Lancia & Luigi De Giovanni & Guglielmo Lulli, 2019. "Data Analytics for Trajectory Selection and Preference-Model Extrapolation in the European Airspace," Operations Research Proceedings, in: Bernard Fortz & Martine Labbé (ed.), Operations Research Proceedings 2018, pages 563-570, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-18500-8_70
    DOI: 10.1007/978-3-030-18500-8_70
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