IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i4p315-d493982.html
   My bibliography  Save this article

A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit Problem

Author

Listed:
  • Andrés Leiva-Araos

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2241, Chile
    These authors contributed equally to this work.)

  • Héctor Allende-Cid

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2241, Chile
    These authors contributed equally to this work.)

Abstract

Most humans today have mobile phones. These devices are permanently collecting and storing behavior data of human society. Nevertheless, data processing has several challenges to be solved, especially if it is obtained from obsolete technologies. Old technologies like GSM and UMTS still account for almost half of all devices globally. The main problem in the data is known as neighboring network hit (NNH). An NNH occurs when a cellular device connects to a site further away than it corresponds to by network design, introducing an error in the spatio-temporal mobility analysis. The problems presented by the data are mitigated by eliminating erroneous data or diluting them statistically based on increasing the amount of data processed and the size of the study area. None of these solutions are effective if what is sought is to study mobility in small areas (e.g., Covid-19 pandemic). Elimination of complete records or traces in the time series generates deviations in subsequent analyses; this has a special impact on reduced spatial coverage studies. The present work is an evolution of the previous approach to NNH correction (NFA) and travel inference (TCA), based on binary logic. NFA and TCA combined deliver good travel counting results compared to government surveys (2.37 vs. 2.27, respectively). However, its main contribution is given by the increase in the precision of calculating the distances traveled (37% better than previous studies). In this document, we introduce FNFA and FTCA. Both algorithms are based on fuzzy logic and deliver even better results. We observed an improvement in the trip count (2.29, which represents 2.79% better than NFA). With FNFA and FTCA combined, we observe an average distance traveled difference of 9.2 km, which is 9.8% better than the previous NFA-TCA. Compared to the naive methods (without fixing the N N H s ), the improvement rises from 28.8 to 19.6 km (46.9%). We use duly anonymized data from mobile devices from three major cities in Chile. We compare our results with previous works and Government’s Origin and Destination Surveys to evaluate the performance of our solution. This new approach, while improving our previous results, provides the advantages of a model better adapted to the diffuse condition of the problem variables and shows us a way to develop new models that represent open challenges in studies of urban mobility based on cellular data (e.g., travel mode inference).

Suggested Citation

  • Andrés Leiva-Araos & Héctor Allende-Cid, 2021. "A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit Problem," Mathematics, MDPI, vol. 9(4), pages 1-36, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:315-:d:493982
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/4/315/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/4/315/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gakenheimer, Ralph, 1999. "Urban mobility in the developing world," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 671-689.
    2. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    3. Laetitia Gauvin & Michele Tizzoni & Simone Piaggesi & Andrew Young & Natalia Adler & Stefaan Verhulst & Leo Ferres & Ciro Cattuto, 2020. "Gender gaps in urban mobility," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    4. Kuwahara, Masao & Sullivan, Edward C., 1987. "Estimating origin-destination matrices from roadside survey data," Transportation Research Part B: Methodological, Elsevier, vol. 21(3), pages 233-248, June.
    5. Lambiotte, Renaud & Blondel, Vincent D. & de Kerchove, Cristobald & Huens, Etienne & Prieur, Christophe & Smoreda, Zbigniew & Van Dooren, Paul, 2008. "Geographical dispersal of mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5317-5325.
    6. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    7. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Erlström & Markus Grillitsch & Ola Hall, 2022. "The geography of connectivity: a review of mobile positioning data for economic geography," Journal of Geographical Systems, Springer, vol. 24(4), pages 679-707, October.
    2. Chaogui Kang & Yu Liu & Diansheng Guo & Kun Qin, 2015. "A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-11, November.
    3. Claudio Gariazzo & Armando Pelliccioni & Maria Paola Bogliolo, 2019. "Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy," Data, MDPI, vol. 4(1), pages 1-25, January.
    4. D. Woods & A. Cunningham & C. E. Utazi & M. Bondarenko & L. Shengjie & G. E. Rogers & P. Koper & C. W. Ruktanonchai & E. zu Erbach-Schoenberg & A. J. Tatem & J. Steele & A. Sorichetta, 2022. "Exploring methods for mapping seasonal population changes using mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    5. Alejandro Llorente & Manuel Garcia-Herranz & Manuel Cebrian & Esteban Moro, 2015. "Social Media Fingerprints of Unemployment," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    6. Przemyslaw A Grabowicz & José J Ramasco & Bruno Gonçalves & Víctor M Eguíluz, 2014. "Entangling Mobility and Interactions in Social Media," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
    7. Alessia Calafiore & Krasen Samardzhiev & Francisco Rowe & Martin Fleischmann & Daniel Arribas-Bel, 2024. "Inequalities in experiencing urban functions. An exploration of human digital (geo-)footprints," Environment and Planning B, , vol. 51(7), pages 1463-1477, September.
    8. Daniel Austin & Robin M Cross & Tamara Hayes & Jeffrey Kaye, 2014. "Regularity and Predictability of Human Mobility in Personal Space," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
    9. Fernando Santa & Roberto Henriques & Joaquín Torres-Sospedra & Edzer Pebesma, 2019. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments," Sustainability, MDPI, vol. 11(3), pages 1-29, January.
    10. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    11. Contreras, Hugo Alejandro & Candia, Cristian & Olchevskaia, Rodrigo Vladislav Troncoso & Ferres, Leo & Celedón, María Loreto Bravo & Lepri, Bruno & Rodriguez-Sickert, Carlos, 2023. "Linking Physical Violence to Women's Mobility in Chile," SocArXiv uad59, Center for Open Science.
    12. Thomas Collins & Riccardo Di Clemente & Mario Gutiérrez-Roig & Federico Botta, 2024. "Spatiotemporal gender differences in urban vibrancy," Environment and Planning B, , vol. 51(7), pages 1430-1446, September.
    13. Radosław Klimek, 2019. "Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    14. Huang, Zhiren & Wang, Pu & Zhang, Fan & Gao, Jianxi & Schich, Maximilian, 2018. "A mobility network approach to identify and anticipate large crowd gatherings," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 147-170.
    15. Levy, Moshe & Goldenberg, Jacob, 2014. "The gravitational law of social interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 418-426.
    16. Tobias Scholl & Antonios Garas & Frank Schweitzer, 2015. "The spatial component of R&D networks," Papers 1509.08291, arXiv.org.
    17. Sanja Šćepanović & Igor Mishkovski & Pan Hui & Jukka K Nurminen & Antti Ylä-Jääski, 2015. "Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    18. Beckers, Joris & Vanhoof, Maarten & Verhetsel, Ann, 2019. "Returning the particular: Understanding hierarchies in the Belgian logistics system," Journal of Transport Geography, Elsevier, vol. 76(C), pages 315-324.
    19. Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    20. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:315-:d:493982. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.