IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v118y2024ics096669232400139x.html
   My bibliography  Save this article

Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring

Author

Listed:
  • Nalin, Alessandro
  • Vignali, Valeria
  • Lantieri, Claudio
  • Cappellari, Denis
  • Zamengo, Bruno
  • Simone, Andrea

Abstract

Veracity is a critical dimension of Big Data, as it is related to the quality of data. Its role is even more important when Big Data are supposed to be a counterpart or a substitute of official data. While the former is usually unstructured and the collecting procedures are unsupervised, the latter is collected in accordance to strict and rigorous methodologies. Mobile phone traces, alternatively called Cellphone Big Data (CBD), can be ascribed among the most popular Big Data typology in transportation analyses, even if they are affected by some biases. This research effort is aimed to contribute to the discussion on Big Data and to shed light on the need of a rigorous assessment of the dataset quality. An in-depth evaluation process was carried out with the comparison of CBD to groundtruth data, namely traffic-related data collected by Anas S.p.A. – Gruppo Ferrovie dello Stato Italiane along a major Italian trunk road. What emerges from this paper is the sensitiveness of CBD to some variables related to both cinematic characteristics of traffic, mobile phone network characteristics and the traffic condition, namely the vehicle occupancy rate.

Suggested Citation

  • Nalin, Alessandro & Vignali, Valeria & Lantieri, Claudio & Cappellari, Denis & Zamengo, Bruno & Simone, Andrea, 2024. "Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring," Journal of Transport Geography, Elsevier, vol. 118(C).
    • Handle: RePEc:eee:jotrge:v:118:y:2024:i:c:s096669232400139x
    DOI: 10.1016/j.jtrangeo.2024.103930
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096669232400139X
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2024.103930?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Šveda, Martin & Madajová, Michala Sládeková, 2023. "Estimating distance decay of intra-urban trips using mobile phone data: The case of Bratislava, Slovakia," Journal of Transport Geography, Elsevier, vol. 107(C).
    2. L.J.J. Wismans & K. Friso & J. Rijsdijk & S.W. de Graaf & J. Keij, 2018. "Improving A Priori Demand Estimates Transport Models using Mobile Phone Data: A Rotterdam-Region Case," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(2), pages 63-83, April.
    3. 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.
    4. Steenbruggen, John & Tranos, Emmanouil & Rietveld, Piet, 2016. "Traffic incidents in motorways: An empirical proposal for incident detection using data from mobile phone operators," Journal of Transport Geography, Elsevier, vol. 54(C), pages 81-90.
    5. Cohen, Maxime C. & Jacquillat, Alexandre & Ratzon, Avia & Sasson, Roy, 2022. "The impact of high-occupancy vehicle lanes on carpooling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 186-206.
    6. Shiraki, Hiroto & Matsumoto, Ken'ichi & Shigetomi, Yosuke & Ehara, Tomoki & Ochi, Yuki & Ogawa, Yuki, 2020. "Factors affecting CO2 emissions from private automobiles in Japan: The impact of vehicle occupancy," Applied Energy, Elsevier, vol. 259(C).
    7. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    8. Hadachi, Amnir & Pourmoradnasseri, Mozhgan & Khoshkhah, Kaveh, 2020. "Unveiling large-scale commuting patterns based on mobile phone cellular network data," Journal of Transport Geography, Elsevier, vol. 89(C).
    9. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    10. Pavol Kuchár & Rastislav Pirník & Aleš Janota & Branislav Malobický & Jozef Kubík & Dana Šišmišová, 2023. "Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    11. Levine, Ned & Wachs, Martin, 1998. "Factors affecting vehicle occupancy measurement," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(3), pages 215-229, April.
    12. Linlin Wu & Guangming Shou & Zaichun Xie & Peng Jing, 2023. "Mobile Phone Data Feature Denoising for Expressway Traffic State Estimation," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    13. Qian, Chen & Li, Weifeng & Duan, Zhengyu & Yang, Dongyuan & Ran, Bin, 2021. "Using mobile phone data to determine spatial correlations between tourism facilities," Journal of Transport Geography, Elsevier, vol. 92(C).
    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. Julia Eichholz & Thorsten Knauer & Sandra Winkelmann, 2023. "Digital Maturity of Forecasting and its Impact in Times of Crisis," Schmalenbach Journal of Business Research, Springer, vol. 75(4), pages 443-481, December.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Isik, Mine & Ari, Izzet & Sarica, Kemal, 2021. "Challenges in the CO2 emissions of the Turkish power sector: Evidence from a two-level decomposition approach," Utilities Policy, Elsevier, vol. 70(C).
    5. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    6. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    7. Spencer Leitch & Zhiyuan Wei, 2024. "Improving spatial access to healthcare facilities: an integrated approach with spatial analysis and optimization modeling," Annals of Operations Research, Springer, vol. 341(2), pages 1057-1074, October.
    8. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    9. 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.
    10. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    11. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    12. Weronika Jakubczak & Anna Golebiowska & Dariusz Prokopowicz, 2021. "The Legal and Security Aspects of ICT and Industry 4.0 Importance for Financial Industry 4.0 Development," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 169-181.
    13. Gianfranco Marotta & Phillipe Krahnhof & Cam-Duc Au, 2022. "A Critical Analysis of Budgeting Processes from the Pharmaceutical Industry and Beyond," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(3), pages 1-3.
    14. 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.
    15. Dan Zhang & Loo G. Pee & Shan L. Pan & Jingyuan Wang, 2024. "Information practices in data analytics for supporting public health surveillance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(1), pages 79-93, January.
    16. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    17. Andrea Cappelli & Iacopo Cavallini, 2021. "The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 53-74.
    18. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    19. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    20. Fernando López & Konstatin Kholodilin, 2023. "Putting MARS into space. Non‐linearities and spatial effects in hedonic models," Papers in Regional Science, Wiley Blackwell, vol. 102(4), pages 871-896, August.

    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:eee:jotrge:v:118:y:2024:i:c:s096669232400139x. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.