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In my 2021 survey on deep learning for traffic congestion detection, prediction, and alleviation, I emphasized a recurring challenge in transportation AI: the lack of open datasets, open-source implementations, and reproducible benchmarks.

Since then, I have tried to make openness a consistent part of my own research practice. For papers where I am a first author or co-author, I aim to share the associated code whenever possible. I also try to use freely available datasets when they are suitable for the research question, so that the work can be reproduced, compared, extended, and used by a wider community.

This page collects paper-specific code repositories, dataset links, reproducibility notes, and related resources.

Update in progress: I am currently curating and standardizing links, documentation quality, and reproducibility notes across papers. A structured list will be published here soon.

Paper-Specific Repositories

This section is being updated with verified links and reproducibility metadata for each publication.

Openness and Reproducibility

I try to follow three principles when sharing research outputs:

1. Release paper-specific code whenever possible.
2. Use freely available datasets when they are suitable for the research question.
3. Document enough of the workflow so that others can reproduce, compare, or extend the work.

Some projects may not include full code or data because of licensing, collaborator restrictions, proprietary inputs, or privacy constraints. In such cases, I try to provide as much information as possible about the methodology, data sources, and reproducibility limitations.