Identifying movements in noisy crowd analytics data

Cristian Chilipirea, Ciprian Dobre, Mitra Baratchi, Martinus Richardus van Steen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
    15 Downloads (Pure)

    Abstract

    Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.
    Original languageEnglish
    Title of host publication19th International Conference on Mobile Data Management (MDM)
    PublisherIEEE
    Pages161-166
    ISBN (Electronic)978-1-5386-4133-0
    DOIs
    Publication statusPublished - 16 Jul 2018
    Event19th IEEE International Conference on Mobile Data Management 2018 - Aalborg, Denmark
    Duration: 26 Jun 201828 Jun 2018
    Conference number: 19
    http://mdmconferences.org/mdm2018/

    Conference

    Conference19th IEEE International Conference on Mobile Data Management 2018
    Abbreviated titleIEEE MDM 2018
    CountryDenmark
    CityAalborg
    Period26/06/1828/06/18
    Internet address

    Fingerprint

    Global positioning system
    Smartphones
    Trajectories

    Cite this

    Chilipirea, C., Dobre, C., Baratchi, M., & van Steen, M. R. (2018). Identifying movements in noisy crowd analytics data. In 19th International Conference on Mobile Data Management (MDM) (pp. 161-166). IEEE. https://doi.org/10.1109/MDM.2018.00033
    Chilipirea, Cristian ; Dobre, Ciprian ; Baratchi, Mitra ; van Steen, Martinus Richardus. / Identifying movements in noisy crowd analytics data. 19th International Conference on Mobile Data Management (MDM). IEEE, 2018. pp. 161-166
    @inproceedings{113228605fd84f09a177b4290446175f,
    title = "Identifying movements in noisy crowd analytics data",
    abstract = "Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.",
    author = "Cristian Chilipirea and Ciprian Dobre and Mitra Baratchi and {van Steen}, {Martinus Richardus}",
    year = "2018",
    month = "7",
    day = "16",
    doi = "10.1109/MDM.2018.00033",
    language = "English",
    pages = "161--166",
    booktitle = "19th International Conference on Mobile Data Management (MDM)",
    publisher = "IEEE",
    address = "United States",

    }

    Chilipirea, C, Dobre, C, Baratchi, M & van Steen, MR 2018, Identifying movements in noisy crowd analytics data. in 19th International Conference on Mobile Data Management (MDM). IEEE, pp. 161-166, 19th IEEE International Conference on Mobile Data Management 2018, Aalborg, Denmark, 26/06/18. https://doi.org/10.1109/MDM.2018.00033

    Identifying movements in noisy crowd analytics data. / Chilipirea, Cristian; Dobre, Ciprian; Baratchi, Mitra ; van Steen, Martinus Richardus.

    19th International Conference on Mobile Data Management (MDM). IEEE, 2018. p. 161-166.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

    T1 - Identifying movements in noisy crowd analytics data

    AU - Chilipirea, Cristian

    AU - Dobre, Ciprian

    AU - Baratchi, Mitra

    AU - van Steen, Martinus Richardus

    PY - 2018/7/16

    Y1 - 2018/7/16

    N2 - Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.

    AB - Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.

    UR - https://www.distributed-systems.net/my-data/papers/2018.mdm.pdf

    U2 - 10.1109/MDM.2018.00033

    DO - 10.1109/MDM.2018.00033

    M3 - Conference contribution

    SP - 161

    EP - 166

    BT - 19th International Conference on Mobile Data Management (MDM)

    PB - IEEE

    ER -

    Chilipirea C, Dobre C, Baratchi M, van Steen MR. Identifying movements in noisy crowd analytics data. In 19th International Conference on Mobile Data Management (MDM). IEEE. 2018. p. 161-166 https://doi.org/10.1109/MDM.2018.00033