Can we trust the AIS destination port information for bulk ships?–Implications for shipping policy and practice
Open accessThe 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
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 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 [21]
The Introduction (page not provided) reports that nearly 40% of AIS destination reports for Capesize and Panamax ships were wrong. That supports the approximate figure, but the paper does not establish a 40% false-positive rate for AIS destination information across ships generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe 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 [21]
The Introduction reports that nearly 40% of destination reports for the studied ships were wrong. But neither the Introduction nor the Conclusions addresses whether those errors cause an underestimation of the RSC’s negative impact; searches for RSC and underestimation found no such passage. Page numbers were not available in our copy. (LLM, conf=0.74)
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.
Cites
Nachum, L. (2001) · Management International Review · Cited by 561 · see citing works
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 · see citing works
Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. (2019) · Transport Reviews · doi:10.1080/01441647.2019.1649315 · Cited by 461 · see citing works
Self-citationAutomatic 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 · see citing works
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 · see citing works
Wood, J., & Dykes, J. A. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2008.165 · Cited by 159 · see citing works
Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density
Li, H., Liu, J., Wu, K., Yang, Z., Liu, R. W., & Xiong, N. N. (2018) · IEEE Access · doi:10.1109/access.2018.2866364 · Cited by 155 · see citing works
Fuzzy logic based approach for ship-bridge collision alert system
Wu, B., Yip, T. L., Yan, X., et al. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.106152 · Cited by 147 · see citing works
Pira, M. L., Marcucci, E., Gatta, V., Ignaccolo, M., Inturri, G., & Pluchino, A. (2017) · European Transport Research Review · doi:10.1007/s12544-017-0268-2 · Cited by 132 · see citing works
Anomaly detection for sea surveillance
Laxhammar, R. (2008) · Cited by 131 · see citing works
Fournier, M., Hilliard, C., Rezaee, S., & Pelot, R. P. (2018) · WMU Journal of Maritime Affairs · doi:10.1007/s13437-018-0151-6 · Cited by 122 · see citing works
Abadie, L. M., Goicoechea, N., & Galarraga, I. (2017) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2017.09.017 · Cited by 115 · see citing works
AIS data driven general vessel destination prediction: A random forest based approach
Zhang, C., Bin, J., Wang, W., et al. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102729 · Cited by 114 · see citing works
Vessel Trajectory Prediction Using Historical Automatic Identification System Data
Alizadeh, D., Alesheikh, A. A., & Sharif, M. (2020) · Journal of Navigation · doi:10.1017/s0373463320000442 · Cited by 91 · see citing works
Assessment of cost as a function of abatement options in maritime emission control areas
Lindstad, E., Sandaas, I., & Strømman, A. H. (2015) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2015.04.018 · Cited by 86 · see citing works
Bakdi, A., Glad, I. K., Vanem, E., et al. (2019) · Journal of Marine Science and Engineering · doi:10.3390/jmse8010005 · Cited by 80 · see citing works
Flag states and safety: 1997-1999
Alderton, T., & Winchester, N. (2002) · Maritime Policy & Management · doi:10.1080/03088830110090586 · Cited by 73 · see citing works
Big maritime data for the Baltic Sea with a focus on the winter navigation system
Lensu, M. J., & Goerlandt, F. (2019) · Marine Policy · doi:10.1016/j.marpol.2019.02.038 · Cited by 69 · see citing works
Bai, X., & Lam, J. S. L. (2018) · Energy Economics · doi:10.1016/j.eneco.2018.10.032 · Cited by 68 · see citing works
The influence of charterers and owners on bulk shipping freight rates
Ådland, R., Cariou, P., & Wolff, F. (2016) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2015.11.014 · Cited by 61 · see citing works
Stable vessel-cargo matching in dry bulk shipping market with price game mechanism
Peng, Z., Shan, W., Guan, F., & Yu, B. (2016) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2016.08.007 · Cited by 59 · see citing works
Gao, M., & Shi, G. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107922 · Cited by 56 · see citing works
Collision avoidance systems in air and maritime traffic
Baldauf, M., Benedict, K., Fischer, S., et al. (2011) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x11408973 · Cited by 55 · see citing works
Does fuel efficiency pay? Empirical evidence from the drybulk timecharter market revisited
Ådland, R., Alger, H., Banyte, J., & Jia, H. (2016) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2016.11.007 · Cited by 48 · see citing works
An integrated approach for visual analysis of a multisource moving objects knowledge base
Willems, N., van Hage, W. R., de Vries, G., et al. (2010) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2010.515029 · Cited by 39 · see citing works
Papapostolou, N. C., Pouliasis, P. K., & Kyriakou, I. (2017) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2017.05.007 · Cited by 36 · see citing works
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 · see citing works
Procházka, V., Ådland, R., & Wolff, F. (2019) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.09.009 · Cited by 30 · see citing works
The Integrity of Information Received by Means of AIS During Anti-collision Manoeuvring
Felski, A., & Jaskólski, K. (2013) · TransNav the International Journal on Marine Navigation and Safety of Sea Transportation · doi:10.12716/1001.07.01.12 · Cited by 26 · see citing works
The eye in the sky – Freight rate effects of tanker supply
Regli, F., & Nomikos, N. K. (2019) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2019.03.015 · Cited by 25 · see citing works
A gam assessment of quality premia in the dry bulk time–charter market
Köhn, S., & Thanopoulou, H. A. (2011) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2011.01.003 · Cited by 24 · see citing works
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 · see citing works
Greig, N. C., Hines, E. M., Cope, S., et al. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00109 · Cited by 18 · see citing works
Identifying Port Calls of Ships by Uncertain Reasoning with Trajectory Data
Wu, L., Xu, Y., & Wang, F. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9120756 · Cited by 15 · see citing works
Self-citationSpatial variation of travel time uncertainty in container shipping
Jia, H., Lam, J. S. L., & Tran, N. K. (2020) · Transportation research procedia · doi:10.1016/j.trpro.2020.08.210 · Cited by 10 · see citing works
The value of foresight in the drybulk freight market
Procházka, V., Ådland, R., & Wallace, S. W. (2019) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.07.003 · Cited by 7 · see citing works
Bradley, J., Neil, A., Zandipour, M., et al. (2009) · doi:10.5772/6585 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- IALA., 2004. IALA guideline no. 1028 on the Automatic Identification System (AIS) volume 1, part I operational issues edition 1.3. https://www.e-navigation.nl/sites/default/files/universal-automatic-identification-ais-volume-1-part-1-operational-issues-1028.pdf.
- UNCTAD, 2019. Review of maritime transportation 2019. Paper presented at the United Nations Conference on Trade and Development (New York and Geneva). http://unctad.org/en/PublicationsLibrary/rmt2019_en.pdf.
Cited by
Sorted by citations
Red Sea crisis impacts on maritime shipping networks.
Wang, Z., Fang, Z., Yu, J., Hu, X., & Gong, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e40384 · Cited by 12 · see citing works
- Authors
- Yang, D., Wu, L., & Wang, S.
- Affiliations
- Hong Kong Polytechnic University (HK)
- HEC Montréal (CA)
- Corresponding author
- Wu, Lingxiao
- Venue
- Transportation Research Part E: Logistics and Transportation Review
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 149
- Pages
- 102308
- ISSN
- 1366-5545 (ISSN-L)
- OpenAlex
- W3148398330
- Cited by
- 49
- References
- 38
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 12 · 2023: 5 · 2022: 7 · 2021: 4
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Transportation Research Part E Logistics and Transportation Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China
- University Grants Committee Research Grants Council
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Maritime Ports and Logistics
- Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
- Keywords
- Automatic Identification System, data accuracy, Panamax ships, shipping policy, discrete choice models
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
