Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing

Indre Zliobaite, Jaakko Hollmén

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

    3 Citations (Scopus)
    42 Downloads (Pure)

    Abstract

    Pervasiveness of mobile phones and the fact that the phones have sensors make them ideal as personal sensors. Smart phones are equipped with a wide range of motion, location and environment sensors, that allow us to analyze, model
    and predict mobility in urban areas. Raw sensory data is being collected as time-stamped sequences of records, and this data needs to be preprocessed and aggregated before any predictive modeling can be done. This paper presents a
    case study in preprocessing such data, collected by one person over six months period. Our goal with this exploratory pilot study is to discuss data aggregation challenges from machine learning point of view, and identify relevant directions
    for future research in preprocessing mobile sensing data for human mobility analysis.
    Original languageEnglish
    Title of host publicationProceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014
    PublisherCEUR
    Pages309-314
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication

    Publication series

    NameCeur Workshop Proceedings
    PublisherCEUR
    Volume1133
    ISSN (Print)1613-0073
    ISSN (Electronic)1613-0073

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