Mitigating Bias in Big Data for Transportation

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.

Investigating the Role of Big Data in Transportation Safety

“…Interviews were conducted online using synchronous text, except for one who preferred a video call. Interviews were anonymized before analysis or sharing on the Virginia Tech Dataverse ( 6 ).…”

“…This study performs three forms of textual analytics on semi-structured interviews with big data experts to respond to three emerging questions in big data for transportation safety ( 6 ):…”