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A Bibliometric Analysis of Research on Maritime Traffic Data from the Automatic Identification System
Steven D. Meyers1*, Laura C.B.C. Azevedo1, and Mark E. Luther1
1Center for Maritime and Port Studies, College of Marine Science,
University of South Florida, St. Petersburg, FL
*corresponding presenter, smeyers@mail.usf.edu
co-author contact info: laurac8@mail.usf.edu , mluther@usf.edu
Submitted to: Enabling Innovative Science and Technology
Vessel traffic records from the Automatic Identification System (AIS) are a useful source of training data for maritime-related artificial intelligence (AI) systems, including large autonomous vessels. Basic research in this field provides the foundation for development of maritime tools that will impact economic growth and security. The distribution of this effort over time and across the globe is examined through a bibliometric study of publications involving AIS data from 1997-2019. The number of publications, authors, and national affiliations of the authors were found. The annual number of publications have increased from about 5 publications per year before 2003, and then roughly doubling roughly every 5 years after that. Overall, authors affiliated with China contributed to 20% of all publications, followed by the US (9%) and Italy (8%). No other single national affiliation represented more than 6% of publications. Publications with at least one member from any EU country were most common, nominally being 40%±10% of total publications. In 2017 publications with at least one China-affiliated author matched this relative level of output, having risen from about 20% in 2007. From 2017-2019 both EU- and China-affiliated authors were listed on about 3 times the number of articles as US-based authors. There has been little international collaboration in this field, with 82% of all publications written by authors based in a single country. US- and UK-based authors were most likely to have collaborative publications with authors based elsewhere.
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1Center for Maritime and Port Studies, College of Marine Science,
University of South Florida, St. Petersburg, FL
*corresponding presenter, smeyers@mail.usf.edu
co-author contact info: laurac8@mail.usf.edu , mluther@usf.edu
Submitted to: Enabling Innovative Science and Technology
Vessel traffic records from the Automatic Identification System (AIS) are a useful source of training data for maritime-related artificial intelligence (AI) systems, including large autonomous vessels. Basic research in this field provides the foundation for development of maritime tools that will impact economic growth and security. The distribution of this effort over time and across the globe is examined through a bibliometric study of publications involving AIS data from 1997-2019. The number of publications, authors, and national affiliations of the authors were found. The annual number of publications have increased from about 5 publications per year before 2003, and then roughly doubling roughly every 5 years after that. Overall, authors affiliated with China contributed to 20% of all publications, followed by the US (9%) and Italy (8%). No other single national affiliation represented more than 6% of publications. Publications with at least one member from any EU country were most common, nominally being 40%±10% of total publications. In 2017 publications with at least one China-affiliated author matched this relative level of output, having risen from about 20% in 2007. From 2017-2019 both EU- and China-affiliated authors were listed on about 3 times the number of articles as US-based authors. There has been little international collaboration in this field, with 82% of all publications written by authors based in a single country. US- and UK-based authors were most likely to have collaborative publications with authors based elsewhere.
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About the Presenter
Steven Meyers
Chief Scientist
University of South Florida St. Petersburg
Steven Meyers, Ph.D. (smeyers@usf.edu) is the Chief Scientist at the Center for Maritime and Port Studies, University of South Florida College of Marine Science. His current research focuses on the use of big data to support maritime navigation through mining AIS records and the development of artificial intelligence algorithms to better predict safe port arrival times. He has extensive experience working with oceanographic and meteorological data, numerical ocean circulation models, data analytics, and most recently, machine learning. Steven has published several dozen peer reviewed manuscripts on topics ranging from the Great Red Spot of Jupiter to changes in coastal circulation and mixing due to hurricanes, large-scale human construction, and sea level rise. He joined the USF College of Marine Science in 1998 as research faculty, after serving as the Associate Director for Oceanography at the Center for Ocean-Atmospheric Studies at Florida State University form 1991-1997. He received a doctorate in Physics in from the University of Texas at Austin with a specialization in geophysical fluids.
Presentation
A Bibliometric Analysis of Research on Maritime Traffic Data from the Automatic Identification System
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