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Analysis of institutional authors

Gomez Comendador, Victor FernandoAuthorRodríguez-Sanz ACorresponding AuthorPérez-Castán JaAuthorArnaldo Valdés RAuthorParís Loreiro áAuthor

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October 11, 2020
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Queue behavioural patterns for passengers at airport terminals: A machine learning approach

Publicated to: Journal Of Air Transport Management. 90 101940- - 2021-01-01 90(), DOI: 10.1016/j.jairtraman.2020.101940

Authors:

Rodriguez-Sanz, Alvaro; Fernandez de Marcos, Alberto; Perez-Castan, Javier A; Gomez Comendador, Fernando; Arnaldo Valdes, Rosa; Paris Loreiro, Angel
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Affiliations

Universidad Politécnica de Madrid - Author
‎ Univ Politecn Madrid UPM, Sch Aeronaut & Space Engn ETSIAE, SATAA Dept, Pza Cardenal Cisneros N3, Madrid 28040, Spain - Author

Abstract

© 2020 Elsevier Ltd Passengers go through different handling processes inside airport terminal buildings. The quality of these processes is usually measured by the time passengers require and by the level of comfort experienced by them. We present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of the most critical processes regarding passenger service. The passengers' flow is simulated to obtain queue lengths at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a store-and forward cell-based system, whose parameters have been tuned and validated with real data from observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then implemented to develop different models for each parameter prediction, after a data analysis stage based on statistical and visualization methods. Feature analysis techniques between dependent variables and the target outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers). This could be a key tool for managing passengers demand and optimise the infrastructure's capacity through resource allocation.
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Keywords

AirportProcessesQueuesRandom forestSimulation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal Of Air Transport Management due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Law. Notably, the journal is positioned above the 90th percentile.

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: 1.53. 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)

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

  • WoS: 24
  • Scopus: 21
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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 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: 100 (PlumX).
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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 (RODRIGUEZ SANZ, ALVARO) and Last Author (PARIS LOREIRO, ANGEL).

the author responsible for correspondence tasks has been RODRIGUEZ SANZ, ALVARO.

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