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Analysis of institutional authors

Gordo, VictorCorresponding AuthorPerez-Castan, Javier AAuthorSanz, Luis PerezAuthorSerrano-Mira, LidiaAuthor

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January 12, 2025
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Feasibility of Conflict Prediction of Drone Trajectories by Means of Machine Learning Techniques

Publicated to: AEROSPACE. 11 (12): 1044- - 2024-12-01 11(12), DOI: 10.3390/aerospace11121044

Authors:

Gordo, Victor; Perez-Castan, Javier A; Sanz, Luis Perez; Serrano-Mira, Lidia; Xu, Yan
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Affiliations

Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China - Author
Ingn & Econ Transporte INECO, Madrid 28036, Spain - Author
Univ Politecn Madrid UPM, Sistemas Aerosp Transporte Aereo & Aeropuertos, ETSI Aeronaut & Espacio, Madrid 28040, Spain - Author
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Abstract

The expected number of drone operations in the coming decades, together with the fact that most of them will take place in very-low-level airspace, will lead to a density of drone flights much greater than that of conventional manned aviation. In this context, the number of conflicts (i.e., 4D convergence of drone trajectories below the safe separation minima) will be much more frequent than in manned aviation and, therefore, conventional air traffic management methods or even the specific proposed mechanisms for drone traffic management are unlikely to be able to solve them safely. This paper considers a set of simulated drone trajectories in a high-density urban environment to analyze the applicability of machine learning regression and classification techniques to detect conflicts among such trajectory times in advance of their occurrence in order to provide new methods to manage the expected drone traffic density safely and efficiently. This would not be possible with current drone traffic management solutions. The obtained results suggest that the Random Forest, Artificial Neural Networks and Logistic Regression algorithms could detect nearly all near-collisions up to 10 s before they occur, and the first two algorithms could also detect a significant number of near-collisions more than 60 s earlier.
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Keywords

AirspaceClassificationConflictCongestedDroneMachine learningTácticaTacticalU-spaceUas

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal AEROSPACE due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 16/55, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Aerospace. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Aerospace Engineering.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 8 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/86247/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 120
    • Downloads: 156
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    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: China.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GORDO ARIAS, VICTOR MANUEL) .

    the author responsible for correspondence tasks has been GORDO ARIAS, VICTOR MANUEL.

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