Mitigating Bias in Big Data for Transportation

Greg Phillip Griffin, Megan Mulhall, Chris Simek and William W. Riggs
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Greg Phillip Griffin: The University of Texas at San Antonio

No trbv9, SocArXiv from Center for Open Science

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.

Date: 2020-01-18
New Economics Papers: this item is included in nep-big and nep-tre
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:trbv9

DOI: 10.31219/osf.io/trbv9

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