IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i9p3631-d352721.html
   My bibliography  Save this article

Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities

Author

Listed:
  • Johan Meppelink

    (Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands)

  • Jens Van Langen

    (Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands)

  • Arno Siebes

    (Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands)

  • Marco Spruit

    (Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands)

Abstract

Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies—for one, during weekends—calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information.

Suggested Citation

  • Johan Meppelink & Jens Van Langen & Arno Siebes & Marco Spruit, 2020. "Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities," Sustainability, MDPI, vol. 12(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3631-:d:352721
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/9/3631/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/9/3631/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Mazhar Rathore & Anand Paul & Awais Ahmad & Gwanggil Jeon, 2017. "IoT-Based Big Data: From Smart City towards Next Generation Super City Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 28-47, January.
    2. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    3. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    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. Johannes Stübinger & Lucas Schneider, 2020. "Understanding Smart City—A Data-Driven Literature Review," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    2. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    3. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    4. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    5. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    6. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    7. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    8. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    9. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    10. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region," Journal of Transport Geography, Elsevier, vol. 78(C), pages 41-55.
    11. Khajehnejad, Moein, 2019. "Efficiency of long-range navigation on Treelike fractals," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 102-110.
    12. 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.
    13. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    14. Yifeng Liu & Yuan Lai, 2024. "Analyzing jogging activity patterns and adaptation to public health regulation," Environment and Planning B, , vol. 51(3), pages 670-688, March.
    15. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    16. 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.
    17. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    18. Han Wang & Damien Fay & Kenneth N. Brown & Liam Kilmartin, 2016. "Modelling revenue generation in a dynamically priced mobile telephony service," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(4), pages 711-734, August.
    19. Toru Nakamura & Toru Takumi & Atsuko Takano & Fumiyuki Hatanaka & Yoshiharu Yamamoto, 2013. "Characterization and Modeling of Intermittent Locomotor Dynamics in Clock Gene-Deficient Mice," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-8, March.
    20. Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.

    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:jsusta:v:12:y:2020:i:9:p:3631-:d:352721. 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.