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Analysis of institutional authors

Gonzalez, Ana Belen RodriguezAuthorDiaz, Juan Jose VinagreCorresponding AuthorWilby, Mark RichardAuthor

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September 15, 2024
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Using App Usage Data From Mobile Devices to Improve Activity-Based Travel Demand Models

Publicated to: IEEE Transactions on Big Data. 10 (5): 633-643 - 2024-10-01 10(5), DOI: 10.1109/TBDATA.2024.3366088

Authors:

Gonzalez, ABR; Burrieza-Galan, J; Diaz, JJV; de Castro, IP; Wilby, MR; Cantu-Ros, OG
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Affiliations

NOMMON, Pl Carlos Trias Bertran, Madrid 28020, Spain - Author
Univ Politecn Madrid, Dept Math Appl Informat & Commun Technol, Madrid 28040, Spain - Author

Abstract

In the last years we have seen several studies showing the potential of mobile network data to reconstruct activity and mobility patterns of the population. These data sources allow continuous monitoring of the population with a higher degree of spatial and temporal resolution and at a lower cost compared with traditional methods. However, for certain applications, the spatial resolution of these data sources is still not enough since it typically provides a spatial resolution of hundreds of meters in urban areas and of few kilometers in rural areas. In this article, we fill this gap by proposing a methodology that utilises GPS data from the usage of different applications in mobile devices. This approach improves the spatial precision in the location of activities, previously identified with the mobile network data.
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Keywords

Application usage dataBig dataBig data analyticBig data analyticsCellular telephonesData analyticsDatabasesImage resolutionLocation of activitiesLocation of activityMobile handsetsMobile phone dataMobile phone datumMobile telecommunication systemsMonitoringNetwork dataPopulation statisticsSociologySoft sensorsStatisticsTravel demand modelsUsage dataWireless networks

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Transactions on Big Data 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 18/147, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • WoS: 4
  • Scopus: 5
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Impact and social visibility

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/86312/

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: 216
  • Downloads: 65
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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 (Rodríguez Gonzalez, Ana Belén) .

the author responsible for correspondence tasks has been Vinagre Díaz, Juan José.

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Awards linked to the item

This work was supported in part by Comunidad de Madrid and in part by the European Regional Development Fund, under Grant S-2020/L3-736 (SHAPEMOV). Recommended for acceptance by X. Yang.
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