Mobilitymirror: Bias-adjusted transportation datasets

Luke Rodriguez, Babak Salimi, Haoyue Ping, Julia Stoyanovich, Bill Howe

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We describe customized synthetic datasets for publishing mobility data. Companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. However, these companies are disincentivized from sharing data not only to protect the privacy of individuals (drivers and/or passengers), but also to protect their own competitive advantage. Moreover, demographic biases arising from how the services are delivered may be amplified if released data is used in other contexts. We describe a model and algorithm for releasing origin-destination histograms that removes selected biases in the data using causality-based methods. We compute the origin-destination histogram of the original dataset then adjust the counts to remove undesirable causal relationships that can lead to discrimination or violate contractual obligations with data owners. We evaluate the utility of the algorithm on real data from a dockless bike share program in Seattle and taxi data in New York, and show that these adjusted transportation datasets can retain utility while removing bias in the underlying data.

    Original languageEnglish (US)
    Title of host publicationBig Social Data and Urban Computing - 1st Workshop, BiDU 2018, Revised Selected Papers
    EditorsJonice Oliveira, Claudio M. Farias, Esther Pacitti, Giancarlo Fortino
    PublisherSpringer Verlag
    Pages18-39
    Number of pages22
    ISBN (Print)9783030112370
    DOIs
    StatePublished - 2019
    Event1st Workshop on Big Social Data and Urban Computing, BiDU 2018 - Rio de Janeiro, Brazil
    Duration: Aug 31 2018Aug 31 2018

    Publication series

    NameCommunications in Computer and Information Science
    Volume926
    ISSN (Print)1865-0929

    Other

    Other1st Workshop on Big Social Data and Urban Computing, BiDU 2018
    CountryBrazil
    CityRio de Janeiro
    Period8/31/188/31/18

    ASJC Scopus subject areas

    • Computer Science(all)
    • Mathematics(all)

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  • Cite this

    Rodriguez, L., Salimi, B., Ping, H., Stoyanovich, J., & Howe, B. (2019). Mobilitymirror: Bias-adjusted transportation datasets. In J. Oliveira, C. M. Farias, E. Pacitti, & G. Fortino (Eds.), Big Social Data and Urban Computing - 1st Workshop, BiDU 2018, Revised Selected Papers (pp. 18-39). (Communications in Computer and Information Science; Vol. 926). Springer Verlag. https://doi.org/10.1007/978-3-030-11238-7_2