SafetyLit Journal Details

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Journal of big data analytics in transportation

Abbreviation: J. Big Data Anal. Transp.

Published by: Holtzbrinck Springer Nature Publishing Group

Publisher Location: Dordrecht, Netherlands

Journal Website:
https://link.springer.com/journal/42421/volumes-and-issues

Range of citations in the SafetyLit database: 2021; 3(1) -- 2022; 4(2)

Publication Date Range: 2019 -- 2022

Number of articles from this journal included in the SafetyLit database: 19
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pISSN = 2523-3556 | eISSN = 2523-3564
LCCN = 2021245807 | OCLC = 1099660162

Find a library that holds this journal: http://worldcat.org/issn/25233556

Journal Language(s): English

Title continued by: Data science for transportation [ISSN 948-1368]

Aims and Scope (from publisher): Big Data Analytics in Transportation publishes high-quality original research and reviews in a wide range of topics where data driven methods and AI play a central role in transportation. The goal of the journal is to showcase the latest methodological advances and applications of big data methods in transportation and appropriate implications for policy making. The journal is also interested in the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where big data intelligence can augment other methods in transportation systems planning, operations, freight, safety analysis, transit, safe and sustainable cities and emergency management. There are many emerging questions of relevance on ethical, social and privacy, that are also relevant in this domain. The focus is primarily on analytical data driven methods, high quality application based studies will also be considered. BDAT provides a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with original research articles, review papers and commentary articles.

Articles in database by volume, issue.