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Mitigating Bias in Big Data for Transportation

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  • Griffin, Greg Phillip

    (The University of Texas at San Antonio)

  • Mulhall, Megan
  • Simek, Chris
  • Riggs, William W.

Abstract

Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

Suggested Citation

  • Griffin, Greg Phillip & Mulhall, Megan & Simek, Chris & Riggs, William W., 2020. "Mitigating Bias in Big Data for Transportation," SocArXiv trbv9_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:trbv9_v1
    DOI: 10.31219/osf.io/trbv9_v1
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