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How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications
Open accessAbstract
The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies.
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“The false-positive rate of destination port information in AIS data can be as high as 40 % [21,22]”
From Red Sea crisis impacts on maritime shipping networks. (2024) · cites this paper as [22]
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“This also leads to an underestimation of the negative impact of the RSC.”
From Red Sea crisis impacts on maritime shipping networks. (2024) · cites this paper as [22]
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Metadata
- Authors
- Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Norwegian School of Economics (NO)
- Chung-Ang University (KR)
- Corresponding author
- Wu, Lingxiao
- Venue
- Transport Reviews
- Publisher
- Informa UK Limited
- Published
- 2019-07-28
- Volume
- 39
- Issue
- 6
- Pages
- 755-773
- ISSN
- 0144-1647 (ISSN-L)
- 1464-5327
- OpenAlex
- W2964482263
- Cited by
- 461
- References
- 53
- Citations by year
- 2026: 65 · 2025: 86 · 2024: 97 · 2023: 72 · 2022: 71 · 2021: 46 · 2020: 22 · 2019: 2
- FWCI
- 24.21
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Transport Reviews (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Maritime Ports and Logistics
- Keywords
- Automatic Identification System, AIS data, navigation safety, trade flow estimation, ship emissions, port performance, Arctic shipping, data mining, causal analysis
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