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Mapping Global Shipping Density from AIS Data

  • Lin Wu (a1) (a2), Yongjun Xu (a1), Qi Wang (a1), Fei Wang (a1) (a2) and Zhiwei Xu (a1)...


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.


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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, 18.
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), 185192.
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, 156168.
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, 373389.
Holsten, S. (2009). Global maritime surveillance with satellite-based AIS. Proceedings of Oceans 2009-Europe, Bremen, Germany, 14.
International Maritime Organization (IMO). (1974). International Convention for the Safety of Life at Sea (SOLAS).
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.
MarineTraffic. (2016a). Ships currently in Range. Accessed 22 February 2016.
MarineTraffic. (2016b). Ships currently in Range.,50. Accessed 22 February 2016.
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.
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, 17.
Marine Management Organisation (MMO). (2014a). Mapping UK shipping density and routes from AIS. Marine Management Organisation.
Marine Management Organisation (MMO). (2014b). Mapping UK shipping density and routes technical annex. Marine Management Organisation.
Natale, F., Gibin, M., Alessandrini, A., Vespe, M. and Paulrud, A. (2015). Mapping Fishing Effort through AIS Data. PloS one, 10(6), e0130746.
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.
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.
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), 22182245.
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, 365369.
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, 1725.
United Nations Conference on Trade and Development (UNCTAD). (2015). Review of Maritime Transport 2015.
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.
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, 915.



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