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Analysis of institutional authors

Perez-Castan, Javier AlbertoCorresponding AuthorPerez-Sanz, LAuthorSerrano-Mira, LidiaAuthorGómez-Comendador, Víctor FernandoCorresponding Author

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February 7, 2022
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Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Publicated to: Aerospace. 9 (2): 67- - 2022-02-01 9(2), DOI: 10.3390/aerospace9020067

Authors:

Perez-Castan, Javier Alberto; Perez-Sanz, Luis; Serrano-Mira, Lidia; Saez-Hernando, Francisco Javier; Rodriguez Gauxachs, Irene; Gomez-Comendador, Victor Fernando
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Affiliations

Univ Politecn Madrid, ETSI Aeronaut & Espacio, Plaza Cardenal Cisneros, Madrid 28008, Spain - Author
Universidad Politécnica de Madrid - Author

Abstract

Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.
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Keywords

atc toolconflict detectionmachine learningAir trafficAtc toolConflict detectionMachine learningRisk assessment

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, 2022, it was in position 8/34, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Aerospace.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.83. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.3 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-12-20, the following number of citations:

  • WoS: 12
  • Scopus: 13
  • Google Scholar: 15
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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 2025-12-20:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 16.
  • 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: 16 (PlumX).

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

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

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/86516/

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: 155
  • Downloads: 44
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Leadership analysis of institutional authors

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 (PEREZ CASTAN, JAVIER ALBERTO) and Last Author (GOMEZ COMENDADOR, VICTOR FERNANDO).

the authors responsible for correspondence tasks have been PEREZ CASTAN, JAVIER ALBERTO and GOMEZ COMENDADOR, VICTOR FERNANDO.

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