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Cited by 461Maritime Navigation and SafetyMaritime Transport Emissions and EfficiencyMaritime Ports and Logistics

How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

Open access
Yang, Dong, Wu, Lingxiao, Wang, Shuaian, Jia, Haiying, Li, Kevin X.·2019·Transport Reviews
DOI 10.1080/01441647.2019.1649315
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Abstract

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%.

    “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]

    Section 2 (Overview of AIS; page unavailable) says manually entered AIS data, including intended destination, can contain errors and inaccuracies. It does not give a false-positive rate for destination-port information or support the claimed 40% figure (searched for “40” and destination-error terms). (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The false-positive rate of destination port information in AIS data also leads to an underestimation of the negative impact of the RSC.

    “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]

    Section 2 (Overview of AIS; page unavailable) says that manually entered AIS information, including intended destination, can be inaccurate. It does not discuss a false-positive rate or say that destination errors cause an underestimation of the RSC’s negative impact. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 4

  1. IALA (2004). IALA Guideline No. 1028 On the Automatic Identification System (AIS) Volume 1, Part I Operational Issues Edition 1.3. Retrieved April 2, 2018 from https://www.e- navigation.nl/sites/default/files/universal-automatic-identification-ais-volume-1-part-1- operational-issues-1028.pdf.
  2. Prochazka, V., & Adland, R. (2017). Insight into oil-tanker market behaviour gained from AIS data. Paper presented at IAME 2017 Conference.
  3. Reeves, R. R., Read, A. J., Lowry, L., Katona, S. K., & Boness, D. J. (2007). Report of the North Atlantic right whale program review. Marine Mammal Commission, Bethesda, Maryland.
  4. Stopford, M. (2018). Leading change into the future for shipping. Leadership 2018 Conference, Bergen, Norway.

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Related

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
ORCID
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
DOI
10.1080/01441647.2019.1649315
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
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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