The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lee, J.-G., Han, J., & Whang, K.-Y. (2007) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1247480.1247546 · Cited by 1,366
The complex network of global cargo ship movements
Kaluza, P., Kölzsch, A., Gastner, M. T., & Blasius, B. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0495 · Cited by 992
Journal of Marine Science and Technology
(2013) · Journal of Marine Science and Technology · doi:10.1007/773.1437-8213 · Cited by 743
Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., & Huang, G.-B. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2724551 · Cited by 521
Automatic Identification System (AIS): Data Reliability and Human Error Implications
Harati-Mokhtari, A., Wall, A., Brooks, P., & Wang, J. (2007) · Journal of Navigation · doi:10.1017/s0373463307004298 · Cited by 460
A Statistical Study of Ship Domains
Goodwin, E. M. (1975) · Journal of Navigation · doi:10.1017/s0373463300041230 · Cited by 454
Perera, L. P., Oliveira, P., & Soares, C. G. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2187282 · Cited by 357
Farré, A. B., Stephenson, S. R., Chen, L., Czub, M. J., Dai, Y. J., Demchev, D., Efimov, Y. O., Graczyk, P., Grythe, H., Keil, K., et al. (2014) · Polar Geography · doi:10.1080/1088937x.2014.965769 · Cited by 313
Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters
Zhang, L., Meng, Q., & Fwa, T. F. (2017) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2017.07.011 · Cited by 220
Empirical Ship Domain based on AIS Data
Hansen, M. G., Jensen, T. K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F. M., & Ennemark, F. (2013) · Journal of Navigation · doi:10.1017/s0373463313000489 · Cited by 214
A quantitative assessment of Arctic shipping in 2010–2014
Eguı́luz, V. M., Fernández-Gracia, J., Irigoien, X., & Duarte, C. M. (2016) · Scientific Reports · doi:10.1038/srep30682 · Cited by 201
Emission inventories for ships in the arctic based on satellite sampled AIS data
Winther, M., Christensen, J. H., Plejdrup, M. S., Ravn, E. S., Eriksson, Ó. F., & Kristensen, H. O. (2014) · Atmospheric Environment · doi:10.1016/j.atmosenv.2014.03.006 · Cited by 163
Zhen, R., Jin, Y., Hu, Q., Shao, Z., & Nikitakos, N. V. (2017) · Journal of Navigation · doi:10.1017/s0373463316000850 · Cited by 162
THE STUDY OF SHIP COLLISION AVOIDANCE ROUTE PLANNING BY ANT COLONY ALGORITHM
Tsou, M.-C., & Hsueh, C. (2010) · Journal of marine science and technology · doi:10.51400/2709-6998.1929 · Cited by 158
Arguedas, V. F., Pallotta, G., & Vespe, M. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2699635 · Cited by 155
Energy efficiency with the application of Virtual Arrival policy
Jia, H., Ådland, R., Prakash, V., & Smith, T. (2017) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2017.04.037 · Cited by 105
Self-citationChen, L., Zhang, D., Ma, X., Wang, L., Li, S., Wu, Z., & Pan, G. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498409 · Cited by 100
Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research
Zhao, Z. Q., Ji, K., Xing, X., Zou, H., & Zhou, S. (2013) · Journal of Navigation · doi:10.1017/s0373463313000659 · Cited by 97
Gerritsen, H. D., Minto, C., & Lordan, C. (2013) · ICES Journal of Marine Science · doi:10.1093/icesjms/fst017 · Cited by 92
Mapping EU fishing activities using ship tracking data
Vespe, M., Gibin, M., Alessandrini, A., Natale, F., Mazzarella, F., & Osio, G. C. (2016) · Journal of Maps · doi:10.1080/17445647.2016.1195299 · Cited by 92
Maritime Anomaly Detection using Gaussian Process Active Learning
Kowalska, K., & Peel, L. (2012) · Research Publications (Maastricht University) · Cited by 90
Research on Ship Classification Based on Trajectory Features
Sheng, K., Liu, Z., Zhou, D., He, A., & Feng, C. (2017) · Journal of Navigation · doi:10.1017/s0373463317000546 · Cited by 90
Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels
Sidibé, A., & Gao, S. (2017) · Journal of Navigation · doi:10.1017/s0373463317000066 · Cited by 78
Are AIS-based trade volume estimates reliable? The case of crude oil exports
Ådland, R., Jia, H., & Strandenes, S. P. (2017) · Maritime Policy & Management · doi:10.1080/03088839.2017.1309470 · Cited by 74
Self-citationDistributed Stochastic Search Algorithm for Multi-ship Encounter Situations
Kim, D., Hirayama, K., & Okimoto, T. (2017) · Journal of Navigation · doi:10.1017/s037346331700008x · Cited by 74
Prevention of oil spill from shipping by modelling of dynamic risk
Eide, M. S., Endresen, Ø., Breivik, Ø., Brude, O. W., Ellingsen, I. H., Røang, K., Hauge, J., & Brett, P. O. (2007) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2007.06.013 · Cited by 73
Reeves, R. R., Rosa, C., George, J. C., Sheffield, G. G., & Moore, M. J. (2011) · Marine Policy · doi:10.1016/j.marpol.2011.08.005 · Cited by 71
Pallotta, G., Horn, S., Braca, P., & Bryan, K. (2014) · Cited by 70
Green approaches at sea – The benefits of adjusting speed instead of anchoring
Andersson, P., & Ivehammar, P. (2017) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2017.01.010 · Cited by 57
Norwegian port connectivity and its policy implications
Jia, H., Lampe, O. D., Šoltészová, V., & Strandenes, S. P. (2017) · Maritime Policy & Management · doi:10.1080/03088839.2017.1366080 · Cited by 50
Self-citationDynamic speed choice in bulk shipping
Ådland, R., & Jia, H. (2016) · Maritime Economics & Logistics · doi:10.1057/s41278-016-0002-3 · Cited by 46
Self-citationEstimating vessel payloads in bulk shipping using AIS data
Jia, H., Prakash, V., & Smith, T. (2018) · International Journal of Shipping and Transport Logistics · doi:10.1504/ijstl.2019.096864 · Cited by 46
Self-citationMaritime Route Delineation using AIS Data from the Atlantic Coast of the US
Breithaupt, S. A., Copping, A. E., Tagestad, J., & Whiting, J. M. (2016) · Journal of Navigation · doi:10.1017/s0373463316000606 · Cited by 42
A Statistical Study of Ship Domains
Goodwin, E. M. (1973) · Journal of Navigation · doi:10.1017/s0373463300022992 · Cited by 42
Contribution of ship traffic to aerosol particle concentrations downwind of a major shipping lane
Kivekäs, N., Maßling, A., Grythe, H., Lange, R. G., Rusnak, V., Carreno, S. D. C., Skov, H., Swietlicki, E., Nguyen, Q., Glasius, M., et al. (2014) · Atmospheric chemistry and physics · doi:10.5194/acp-14-8255-2014 · Cited by 41
The determinants of vessel capacity utilization: The case of Brazilian iron ore exports
Ådland, R., Jia, H., & Strandenes, S. P. (2016) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2016.11.023 · Cited by 41
Self-citationIce and AIS: ship speed data and sea ice forecasts in the Baltic Sea
Löptien, U., & Axell, L. (2014) · The cryosphere · doi:10.5194/tc-8-2409-2014 · Cited by 39
Waterway Performance Monitoring with Automatic Identification System Data
Mitchell, K. N., & Scully, B. M. (2014) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2426-03 · Cited by 37
AIS Data-based Decision Model for Navigation Risk in Sea Areas
Li, L., Lu, W., Niu, J., Liu, J., & Liu, D. (2017) · Journal of Navigation · doi:10.1017/s0373463317000807 · Cited by 37
Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering
Wang, J., Zhu, C., Zhou, Y., & Zhang, W. (2017) · Journal of Navigation · doi:10.1017/s0373463317000406 · Cited by 36
Data Fusion for Wide-Area Maritime Surveillance
Mazzarella, F., Alessandrini, A., Herman, V. W. G., Marlene, A. A., Argentieri, P., Domenico, N., & Wojciech, Z. L. (2013) · Joint Research Centre (European Commission) · Cited by 36
Campana, I., Angeletti, D., Crosti, R., Luperini, C., Ruvolo, A., Alessandrini, A., & Arcangeli, A. (2016) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2016.12.008 · Cited by 35
Farhadi, N., Parr, S. A., Mitchell, K. N., & Wolshon, B. (2016) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2549-02 · Cited by 33
An AIS data Visualization Model for Assessing Maritime Traffic Situation and its Applications
Pan, J., Jiang, Q., Hu, J., & Zheping, S. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2011.12.724 · Cited by 31
Dynamic route planning in the Baltic Sea Region – A cost-benefit analysis based on AIS data
Andersson, P., & Ivehammar, P. (2016) · Maritime Economics & Logistics · doi:10.1057/mel.2016.18 · Cited by 26
Polluter identification with spaceborne radar imagery, AIS and forward drift modeling
Longépé, N., Mouche, A. A., Goacolou, M., Granier, N., Carrère, L., Lebras, J. Y., Lozach, P., & Besnard, S. (2015) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2015.08.006 · Cited by 23
Allen, A. S., Yurk, H., Vagle, S., Pilkington, J. F., & Canessa, R. R. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.01.014 · Cited by 22
An automatic algorithm for generating seaborne transport pattern maps based on AIS
Jia, H., Lampe, O. D., Šoltészová, V., & Strandenes, S. P. (2017) · Maritime Economics & Logistics · doi:10.1057/s41278-017-0075-7 · Cited by 18
Self-citationAase, J. G., & Jabour, J. (2015) · The Polar Journal · doi:10.1080/2154896x.2015.1068534 · Cited by 17
Green Steaming: A Methodology for Estimating Carbon Emissions Avoided
Watson, R. T., Holm, H., & Lind, M. (2015) · International Conference on Information Systems · Cited by 17
Underkeel Clearance Reliability Model for Dredged Navigation Channels
Scully, B. M., & Mitchell, K. N. (2017) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2611-05 · Cited by 15
Estimating vessel payloads in bulk shipping using AIS data
Jia, H., Smith, T., & Prakash, V. (2018) · International Journal of Shipping and Transport Logistics · doi:10.1504/ijstl.2019.10017649 · Cited by 15
Self-citationRainbow Thermometry Based on EMD De-Noising and Signal Analysis
Pan, Q. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.509 · Cited by 4
Research works printed but not linked: 1
- Jia H., 2018, International Journal of Shipping and Transport Logistics
Cited by
Sorted by citations
Satellite Communications in the New Space Era: A Survey and Future Challenges
Kodheli, O., Lagunas, E., Maturo, N., Sharma, S. K., R., B. S. M., Montoya, J. F. M., Duncán, J. C. M., Spano, D., Chatzinotas, S., Kisseleff, S., et al. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3028247 · Cited by 1,596
Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020) · Maritime Policy & Management · doi:10.1080/03088839.2020.1788731 · Cited by 459
Applications of smart technologies in logistics and transport: A review
Chung, S. (2021) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2021.102455 · Cited by 295
Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond
Zhang, X., Fu, X., Xiao, Z., Xu, H., & Qin, Z. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3192574 · Cited by 201
Applications of machine learning methods in port operations – A systematic literature review
Filom, S., Amiri, A. M., & Razavi, S. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102722 · Cited by 220
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
