Dataset of Deep Learning for Intelligent Transportation System: A Systematic Literature Review of Applications, Methods, and Data Modalities

This dataset supports the study “Deep Learning for Intelligent Transportation Systems: A Systematic Literature Review of Applications, Methods, and Data Modalities.” It was compiled from the Scopus and Web of Science databases, covering publications from 2019 to 2024. The initial retrieval yielded 184 records, which were screened following PRISMA guidelines to remove duplicates and irrelevant studies, resulting in 125 publications used for bibliometric analysis and a refined subset of 35 articles for in-depth qualitative review. The dataset includes structured bibliographic metadata such as authors, publication year, source title, country, keywords, and citation information, enabling analysis of research trends, methodological developments, and application domains in DL-based ITS. Data processing and analysis were conducted using Biblioshiny, ensuring reproducibility and transparency.