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Mapping Global Shipping Density from AIS Data

Published online by Cambridge University Press:  06 June 2016

Lin Wu Open the ORCID record for Lin Wu [Opens in a new window] ,
Yongjun Xu ,
Qi Wang ,
Fei Wang  and
Zhiwei Xu
Lin Wu*
Affiliation:
(Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China) (University of Chinese Academy of Sciences, Beijing, China)
Yongjun Xu
Affiliation:
(Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China)
Qi Wang
Affiliation:
(Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China)
Fei Wang
Affiliation:
(Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China) (University of Chinese Academy of Sciences, Beijing, China)
Zhiwei Xu
Affiliation:
(Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China)
*
(E-mail: [email protected])
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Abstract

Mapping global shipping density, including vessel density and traffic density, is important to reveal the distribution of ships and traffic. The Automatic Identification System (AIS) is an automatic reporting system widely installed on ships initially for collision avoidance by reporting their kinematic and identity information continuously. An algorithm was created to account for errors in the data when ship tracks seem to ‘jump’ large distances, an artefact resulting from the use of duplicate identities. The shipping density maps, including the vessel and traffic density maps, as well as AIS receiving frequency maps, were derived based on around 20 billion distinct records during the period from August 2012 to April 2015. Map outputs were created in three different spatial resolutions: 1° latitude by 1° longitude, 10 minutes latitude by 10 minutes longitude, and 1 minute latitude by 1 minute longitude. The results show that it takes only 56 hours to process these records to derive the density maps, 1·7 hours per month on average, including data retrieval, computation and updating of the database.

Keywords

Automatic Identification SystemVessel DensityTraffic DensityAIS Receiving Network Coverage
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 1 , January 2017 , pp. 67 - 81
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Arguedas, V.F., Pallotta, G. and Vespe, M. (2014). Automatic generation of geographical networks for maritime traffic surveillance. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain, 1–8.Google Scholar
Greidanus, H., Alvarez, M., Eriksen, T., Argentieri, P., Çokacar, T., Pesaresi, A., Falchetti, S., Nappo, D., Mazzarella, F. and Alessandrini, A. (2013). Basin-Wide Maritime Awareness From Multi-Source Ship Reporting Data. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 7(2), 185–192.Google Scholar
Greidanus, H., Alvarez, M., Eriksen, T. and Gammieri, V. (2016). Completeness and Accuracy of a Wide-Area Maritime Situational Picture based on Automatic Ship Reporting Systems. Journal of Navigation, 69, 156–168.Google Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic Identification System (AIS): data reliability and human error implications. Journal of Navigation, 60, 373–389.CrossRefGoogle Scholar
Holsten, S. (2009). Global maritime surveillance with satellite-based AIS. Proceedings of Oceans 2009-Europe, Bremen, Germany, 1–4.Google Scholar
International Maritime Organization (IMO). (1974). International Convention for the Safety of Life at Sea (SOLAS).Google Scholar
International Telecommunications Union (ITU-R). (2010). Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band, Recommendation ITU-R M.1371-4.Google Scholar
MarineTraffic. (2016a). Ships currently in Range. http://www.marinetraffic.com/en/ais/index/ships/range/page:1/speed_between:30%2C50. Accessed 22 February 2016.Google Scholar
MarineTraffic. (2016b). Ships currently in Range. http://www.marinetraffic.com/en/ais/index/ships/range/page:1/speed_between:45,50. Accessed 22 February 2016.Google Scholar
MarineTraffic. (2016c). SAR 111224503. http://www.marinetraffic.com/en/ais/details/ships/shipid:3350996/imo:0/mmsi:111224503/vessel:SAR%20111224503. Accessed 22 February 2016.Google Scholar
Mazzarella, F., Alessandrini, A., Greidanus, H., Alvarez, M., Argentieri, P., Nappo, D. and Ziemba, L. (2013). Data Fusion for Wide-Area Maritime Surveillance. Proceedings of : COST MOVE Workshop on Moving Objects at Sea, Brest.Google Scholar
Mazzarella, F., Vespe, M., Damalas, D. and Osio, G. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain, 1–7.Google Scholar
Marine Management Organisation (MMO). (2014a). Mapping UK shipping density and routes from AIS. Marine Management Organisation. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/317770/1066.pdf Google Scholar
Marine Management Organisation (MMO). (2014b). Mapping UK shipping density and routes technical annex. Marine Management Organisation. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/317771/1066-annex.pdf Google Scholar
Natale, F., Gibin, M., Alessandrini, A., Vespe, M. and Paulrud, A. (2015). Mapping Fishing Effort through AIS Data. PloS one, 10(6), e0130746.CrossRefGoogle ScholarPubMed
Pallotta, G., Vespe, M. and Bryan, K. (2013a). Traffic knowledge discovery from AIS data. Proceedings of the 16th International Conference on Information Fusion (FUSION), 1996–2003.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013b). Traffic Route Extraction and Anomaly Detection from AIS Data. Proceedings of the International COST MOVE Workshop on Moving Objects at Sea, Brest, France.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013c). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.CrossRefGoogle Scholar
Pan, J., Jiang, Q., Hu, J. and Shao, Z. (2012). An AIS data Visualization Model for Assessing Maritime Traffic Situation and its Applications. 2012 International Workshop on Information and Electronics Engineering, 29, 365–369.Google Scholar
Shelmerdine, R.L. (2015). Teasing out the detail: How our understanding of marine AIS data can better inform industries, developments, and planning. Marine Policy, 54, 17–25.Google Scholar
United Nations Conference on Trade and Development (UNCTAD). (2015). Review of Maritime Transport 2015.Google Scholar
Vespe, M., Visentini, I., Bryan, K. and Braca, P. (2012). Unsupervised learning of maritime traffic patterns for anomaly detection. Proceedings of 9th Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 1–5.Google Scholar
Vespe, M., Greidanus, H. and Alvarez, M.A. (2015). The declining impact of piracy on maritime transport in the Indian Ocean: Statistical analysis of 5-year vessel tracking data. Marine Policy, 59, 9–15.Google Scholar