Measuring the State of Open Science in Transportation Using Large Language Models

Abstract

Open science initiatives have strengthened scientific integrity across many fields, yet their adoption in transportation research remains under-investigated. Key features of open science—data and code availability—are difficult to extract at scale due to the complexity of full-text publications. We introduce a scalable, LLM-based pipeline to measure data and code availability and validate its performance through inter-rater agreement analysis against a manually curated dataset of 96 papers. Applied to 10,724 research articles published in Transportation Research journals (2019– 2024), we find that only 5% of quantitative papers share a code repository and 4% share a data repository, with patterns varying across journals, topics, and geographic regions. No significant link is found between open science practices and traditional academic incentives, suggesting that structural interventions from publishers and funding agencies are needed. An interactive explorer is available at https://www.rerite.org/MOST.