Welcome
This website hosts interactive pages for the Measuring Open Science in Transportation (MOST) project [1] .
Our research utilizes Large Language Models (LLMs) to analyze and measure the adoption of open science practices across thousands of transportation research papers. This platform allows readers and reviewers to explore our findings and verify the data and code availability state in the field.
Project Highlights
10,000+ Papers
Comprehensive analysis of transportation research articles from major journals from 2019-2024.
LLM-Enabled
Leveraging Large Language Models to automatically detect and classify open science practices with validation.
Open Data & Code
Exploring the evolution of data availability and code sharing trends in the transportation research community.
Available Resources
Data Explorer
An interactive dashboard to explore papers with available data or code, filtered by various criteria.
Open ExplorerProject Team
Junyi Ji
Vanderbilt University
Ruth Lu
MIT
Linda Belkessa
Université Gustave Eiffel
Liming Wang
Portland State University
Silvia Varotto
École Nationale des Travaux Publics de l'État
Yongqi Dong
Delft University of Technology
Nicolas Saunier
Polytechnique Montréal
Mostafa Ameli
Université Gustave Eiffel
Gregory S. Macfarlane
Brigham Young University
Bahman Madadi
École Nationale des Travaux Publics de l'État
Cathy Wu
MIT
How to cite
[1] Ji, J., Lu, R., Belkessa, L., Wang, L., Varotto, S., Dong, Y., Saunier, N., Ameli, M., Macfarlane, G. S., Madadi, B., Wu, C. (2025). Measuring the State of Open Science in Transportation Using Large Language Models. Working paper.
Show BibTeX
@misc{RERITE2026MOST,
title = {Measuring the State of Open Science in Transportation Using Large Language Models},
author = {Ji, Junyi and Lu, Ruth and Belkessa, Linda and Wang, Liming and Varotto, Silvia and Dong, Yongqi and Saunier, Nicolas and Ameli, Mostafa and Macfarlane, Gregory S. and Madadi, Bahman and Wu, Cathy},
note = {Working paper},
year = {2026}
}