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Data analytics for trajectory selection and preference-model extrapolation in the European airspace

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Data analytics for trajectory selection and preference-model extrapolation in the European airspace. / Lancia, Carlo; De Giovanni, Luigi ; Lulli, Guglielmo.

Operations Research Proceedings 2018: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Brussels, Belgium, September 12-14, 2018. ed. / Bernard Fortz; Martine Labbe'. Cham : Springer, 2019. p. 563-570 (Open Research Proceedings).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Lancia, C, De Giovanni, L & Lulli, G 2019, Data analytics for trajectory selection and preference-model extrapolation in the European airspace. in B Fortz & M Labbe' (eds), Operations Research Proceedings 2018: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Brussels, Belgium, September 12-14, 2018. Open Research Proceedings, Springer, Cham, pp. 563-570. https://doi.org/10.1007/978-3-030-18500-8_70

APA

Lancia, C., De Giovanni, L., & Lulli, G. (2019). Data analytics for trajectory selection and preference-model extrapolation in the European airspace. In B. Fortz, & M. Labbe' (Eds.), Operations Research Proceedings 2018: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Brussels, Belgium, September 12-14, 2018 (pp. 563-570). (Open Research Proceedings). Springer. https://doi.org/10.1007/978-3-030-18500-8_70

Vancouver

Lancia C, De Giovanni L, Lulli G. Data analytics for trajectory selection and preference-model extrapolation in the European airspace. In Fortz B, Labbe' M, editors, Operations Research Proceedings 2018: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Brussels, Belgium, September 12-14, 2018. Cham: Springer. 2019. p. 563-570. (Open Research Proceedings). https://doi.org/10.1007/978-3-030-18500-8_70

Author

Lancia, Carlo ; De Giovanni, Luigi ; Lulli, Guglielmo. / Data analytics for trajectory selection and preference-model extrapolation in the European airspace. Operations Research Proceedings 2018: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Brussels, Belgium, September 12-14, 2018. editor / Bernard Fortz ; Martine Labbe'. Cham : Springer, 2019. pp. 563-570 (Open Research Proceedings).

Bibtex

@inproceedings{3655f0aebf294563b2c6670f1fcd64ee,
title = "Data analytics for trajectory selection and preference-model extrapolation in the European airspace",
abstract = "Representing airspace users{\textquoteright} 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 unknownfactors (en-route charges, fuel consumption, weather, etc.). Associationsbetween 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{\textquoteright} preferences.",
keywords = "Air traffic flow management, Data analytics, Mathematical models, Airspace users{\textquoteright} preferences",
author = "Carlo Lancia and {De Giovanni}, Luigi and Guglielmo Lulli",
year = "2019",
month = aug
day = "30",
doi = "10.1007/978-3-030-18500-8_70",
language = "English",
isbn = "9783030184995",
series = "Open Research Proceedings",
publisher = "Springer",
pages = "563--570",
editor = "Bernard Fortz and Martine Labbe'",
booktitle = "Operations Research Proceedings 2018",

}

RIS

TY - GEN

T1 - Data analytics for trajectory selection and preference-model extrapolation in the European airspace

AU - Lancia, Carlo

AU - De Giovanni, Luigi

AU - Lulli, Guglielmo

PY - 2019/8/30

Y1 - 2019/8/30

N2 - 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 unknownfactors (en-route charges, fuel consumption, weather, etc.). Associationsbetween 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.

AB - 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 unknownfactors (en-route charges, fuel consumption, weather, etc.). Associationsbetween 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.

KW - Air traffic flow management

KW - Data analytics

KW - Mathematical models

KW - Airspace users’ preferences

U2 - 10.1007/978-3-030-18500-8_70

DO - 10.1007/978-3-030-18500-8_70

M3 - Conference contribution/Paper

SN - 9783030184995

T3 - Open Research Proceedings

SP - 563

EP - 570

BT - Operations Research Proceedings 2018

A2 - Fortz, Bernard

A2 - Labbe', Martine

PB - Springer

CY - Cham

ER -