Hostname: page-component-745bb68f8f-b6zl4 Total loading time: 0 Render date: 2025-02-11T07:04:14.934Z Has data issue: false hasContentIssue false
  • English
  • Français

Navigation pattern extraction from AIS trajectory big data via topic model

Published online by Cambridge University Press:  10 July 2023

Iwao Fujino  and
Christophe Claramunt
Iwao Fujino*
Affiliation:
School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan
Christophe Claramunt
Affiliation:
Naval Academy Research Institute, French Naval Academy, Lanvéoc-Poulmic, France
*
*Corresponding author: Iwao Fujino; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.

Keywords

automatic identification systemtrajectoryship behaviournavigation
Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 4-5 , July 2023 , pp. 506 - 524
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Avrithis, Y., Kalantidis, Y., Anagnostopoulos, E. and Emiris, I. Z. (2015). Web-Scale Image Clustering Revisited. In: Proceedings of IEEE ICCV. Araucano Park, Chile: IEEE Computer Society.Google Scholar
Best, G. (2011). Satellite-based AIS system provides continuous tracking at sea. Sea Technology, 52(3), 15–17.Google Scholar
Birant, D. and Kut, A. (2007). ST-DBSCAN: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering, 60, 208–221.CrossRefGoogle Scholar
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.Google Scholar
Claramunt, C., et al. (2017). Maritime Data Integration and Analysis: Recent Progress and Research Challenges. In: Proceedings of 20th International Conference on Extending Database Technology (EDBT), Venice, Italy.Google Scholar
Commission of the European Communities. (2008). Common position adopted by the Council with a view to the adoption of a Directive of the European Parliament and of the Council amending Directive 2002/59/EC establishing a Community vessel traffic monitoring and information system, COM (2008) 310 final 2005/0239(COD); Brussels, Belgium, 11 June 2008. Available at: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2008:0310:FIN:EN:pdf.Google Scholar
Dobrkovic, A., Iacob, M. E. and van Hillegersberg, J. (2015). Using Machine Learning for Unsupervised Martime Waypoint Discovery from Streaming AIS Data. In: Proc. i-KNOW’15, Graz, Austria, Oct. 2015. Available at: http://dx.doi.org/10.1145/2809563.2809573.CrossRefGoogle Scholar
Dobrkovic, A., Iacob, M. E. and van Hillegersberg, J. (2018). Maritime pattern extraction and route reconstruction from incomplete AIS data. International Journal of Data Science and Analytics, 5(6), 111–136. https://doi.org/10.1007/s41060-017-0092-8CrossRefGoogle Scholar
Edlund, J., Grönkvist, M., Lingvall, A., Sviestins, E. (2006). Rule-based Situation Assessment for Sea Surveillance. In: Dasarathy, B. V. (ed.). Proceedings of SPIE vol. 6242 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006. Orlando, FL: SPIE, 624203.CrossRefGoogle Scholar
Fujino, I., Claramunt, C. and Boudraa, A. O. (2017). Extracting Route Patterns of Vessels from AIS Data by Using Topic Model. In: Proc. IEEE International Conference on Big Data (BIGDATA2017). Boston, MA: IEEE Computer Society, 4662–4664.Google Scholar
Fujino, I., Claramunt, C. and Boudraa, A. O. (2018). Extracting Courses of Vessels from AIS Data and Real-Time Warning Against Off-Course. In: Proc. 2nd International Conference on Big Data Research (ICBDR2018). Weihai, China: Association for Computing Machinery, 62–69.CrossRefGoogle Scholar
Gong, Y., Pawlowski, M., Yang, F., Brandy, L., Bourdev, L. and Fergus, R. (2015). Web Scale Photo Hash Clustering on A Single Machine. In: Proc. IEEE CVPR. Boston, MA: IEEE Computer Society.Google Scholar
Gray, R. M. (1984). Vector Quantization. IEEE ASSP Magazine, 4–29.CrossRefGoogle Scholar
Hoye, G. K., Eriksen, T., Meland, B. J. and Narheim, B. T. (2008). Space-based AIS for global maritime traffic monitoring. Acta Astronautica, 62, 240–245.CrossRefGoogle Scholar
IMO (1998). RESOLUTION MSC.74(69) (adopted on 12 May 1998) Adoption of New and Amended Performance Standards. Available at: https://wwwcdn.imo.org/localresources/en/OurWork/Safety/Documents/AIS/Resolution%20MSC.74(69).pdf.Google Scholar
IMO (2000). RESOLUTION MSC.99(73) (adopted on 5 December 2000) Adoption of Amendments to the International Convention for the Safety of Life at Sea, 1974, as Amended. Available at: https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/MSCResolutions/MSC.99(73).pdf.Google Scholar
Jégou, H., Douze, M. and Schmid, C. (2011). Product Quantization for Nearest Neighbor Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1), 117–128.CrossRefGoogle ScholarPubMed
Laxhammer, R. (2008). Anomaly Detection for Sea Surveillance. In: Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany.Google Scholar
Likas, A., Vlassis, N. and Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36, 451–461.CrossRefGoogle Scholar
Linde, Y., Buzo, A. and Gray, R. M. (1980). An algorithm for vector quantiser design. IEEE Transactions on Communications, 702–710.Google Scholar
Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE TIT, 28(2), 29–137.Google Scholar
Matsui, Y., Ogaki, K., Yamasaki, T. and Aizawa, K. (2017). PQk-means: Billion-Scale Clustering for Product-Quantised Codes. In: Proceedings of the 25th ACM International Conference on Multimedia. Mountain View, CA: ACM Computer Society, 1725–1733.Google Scholar
MBDW (2020). 2nd Maritime Big Data Workshop. https://sites.google.com/view/mbdw2020Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discover from AIS data: a framework for anomaly detection and route prediction. Entropy, 15, 2288–2315.CrossRefGoogle Scholar
Piciarelli, C., Nicheloni, C. and Foresti, G. L. (2008). Trajectory-based anomalous event detection. IEEE Transactions on Circuits and Systems for Video Technology, 18, 1544–1554.CrossRefGoogle Scholar
Ray, C., Grancher, A., Thibaud, R., Etienne, L. (2013). Spatio-Temporal Rule-based Analysis of Maritime Traffic. Third Conference on Ocean & Coastal Observation: Sensors and Observing Systems, Numerical Models and Information (OCOSS), Nice, France. hal-01627352.Google Scholar
Ray, C., Dréo, R., Camossi, E. and Jousselme, A. L. (2018). Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance (0.1) [Data set]. Zenodo. Available at: https://doi.org/10.5281/zenodo.1167595.CrossRefGoogle Scholar
Ristic, B., La Scala, B., Morelande, M. and Gordon, N. (2008). Statistical Analysis of Motion Patterns in AIS Data: Anomaly Detection and Motion Prediction. In: Proceedings of the 11th International Conference on Information Fusion. Cologne, Germany.Google Scholar
U.S. Coast Guard Navigation Center (2000). AIS Requirements. https://www.navcen.uscg.gov/?pageName=AISRequirementsRev#.Google Scholar
Vries, G. K. D. and van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications. http://doi.org/10.1016/j.eswa.2012.05.060Google Scholar
Zhang, B., Hirayama, K., Ren, H., Wang, D., Li, H. (2023). Ship anomalous behavior detection using clustering and deep recurrent neural network. Journal of Marine Science and Engineering, 11, 763. https://doi.org/10.3390/jmse11040763CrossRefGoogle Scholar