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.
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Acknowledgements
The authors appreciate interview coding support by Boya Dai with the Texas A&M Transportation Institute, and insightful comments from four reviewers. This project was supported by the Safety through Disruption (Safe-D) National University Transportation Center, a grant from the US Department of Transportation—Office of the Assistant Secretary for Research and Technology, University Transportation Centers Program.
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Griffin, G.P., Mulhall, M., Simek, C. et al. Mitigating Bias in Big Data for Transportation. J. Big Data Anal. Transp. 2, 49–59 (2020). https://doi.org/10.1007/s42421-020-00013-0
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DOI: https://doi.org/10.1007/s42421-020-00013-0