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MobilityMirror: Bias-Adjusted Transportation Datasets

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Big Social Data and Urban Computing (BiDU 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 926))

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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.

J. Stoyanovich—This work was supported in part by NSF Grant No. 1741047.

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Notes

  1. 1.

    Many methods for comparing ranked lists have been proposed. We opt for a measure in which identity of the items being ranked (histogram buckets) is deemed important. This is in contrast to typical IR measures such as NDCG or MAP, where item identity is disregarded, and only item quality or relevance scores are retained.

  2. 2.

    Two datasets are neighbors if they differ in the presence or absence of a single record, following the differential privacy definition.

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Author information

Authors and Affiliations

  1. University of Washington, Seattle, USA

    Luke Rodriguez, Babak Salimi & Bill Howe

  2. Drexel University, Philadelphia, USA

    Haoyue Ping

  3. New York University, New York City, USA

    Julia Stoyanovich

Authors
  1. Luke Rodriguez
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  2. Babak Salimi
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  3. Haoyue Ping
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  4. Julia Stoyanovich
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  5. Bill Howe
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Corresponding author

Correspondence to Luke Rodriguez .

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Editors and Affiliations

  1. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

    Jonice Oliveira

  2. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Claudio M. Farias

  3. Inria/CNRS, University of Montpellier, Montpellier, France

    Esther Pacitti

  4. University of Calabria (Unical), Rende (CS), Italy

    Giancarlo Fortino

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Rodriguez, L., Salimi, B., Ping, H., Stoyanovich, J., Howe, B. (2019). MobilityMirror: Bias-Adjusted Transportation Datasets. In: Oliveira, J., Farias, C., Pacitti, E., Fortino, G. (eds) Big Social Data and Urban Computing. BiDU 2018. Communications in Computer and Information Science, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-11238-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-11238-7_2

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  • Print ISBN: 978-3-030-11237-0

  • Online ISBN: 978-3-030-11238-7

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