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Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Published online by Cambridge University Press:  26 September 2018

Luis Felipe Sánchez-Heres*
Affiliation:
(Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden)
*

Abstract

Two pre-processes for Automatic Identification System (AIS) trajectories commonly reported in the maritime knowledge discovery literature are trajectory simplification and event identification. Both pre-processes reduce storage and computational expenses by reducing the number of data points to be used in an analysis. This paper presents an event identification and trajectory simplification method based on behaviour identification and translation. Trajectory segments deemed to correspond to coastal or ocean navigation are translated into equivalent passage plan segments; a succinct description of the movements and behaviour of the ship. As a trajectory simplification method, it provides two main advantages over commonly used trajectory simplification methods: more meaningful simplified trajectories with better encoding of basic behaviours and the possibility to retain interesting behaviours in full resolution. As an event identification method, it is capable of differentiating between normal ocean or coastal navigating behaviour and complex or interesting behaviour, such as pilotage, reaction to a traffic conflict, or an involuntary deviation from the passage plan.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Berminghan, L. and Lee, I. (2017). A framework of spatio-temporal trajectory simplification methods. International Journal of Geographical Information Science, 31(6), 11281153.Google Scholar
Buchin, M., Driemel, A., Van Kreveld, M. and Sacristán, V. (2011). Segmenting trajectories: A framework and algorithms using spatiotemporal criteria. Journal of Spatial Information Science, 3, 3363.Google Scholar
Cazzanti, L. and Pallota, G. (2015). Mining Maritime Vessel Traffic: Promises, Challenges, Techniques. OCEANS 2015-Genova, Genova, Italy. IEEE. 16.Google Scholar
de Souza, E. N., Boerder, K., Matwin, S. and Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. PloS one, 11(7), e0158248.Google Scholar
Dhar, S. (2016). Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System. Doctoral Dissertation, University of Toledo.Google Scholar
Douglas, D.H. and Peucker, T.K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica, 10(2), 112122.Google Scholar
Feng, Z. and Zhu, Y. (2016). A survey on Trajectory Data Mining: Techniques and Applications. IEEE Access, 4, 20562067.Google Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic Identification System (AIS): data reliability and human error implications. The Journal of Navigation, 60(3), 373389.Google Scholar
Hu, Q., Cai, F., Yang, C. and Shi, C. J. (2014). An algorithm for interpolating ship motion vectors. International Journal on Marine Navigation and Safety of Sea Transportation, 8(1), 3540.Google Scholar
Huang, C., Hu, S., Kong, F. and Xi, Y. (2017). Pre-warning system analysis on dynamic risk of ship collision with bridge at restricted waters. 4th International Conference on Transportation Information and Safety (ICTIS), Alberta, Canada. IEEE.Google Scholar
Iphar, C., Napoli, A. and Ray, C. (2015). Detection of false AIS messages for the improvement of maritime situational awareness. In OCEANS'15 MTS/IEEE, Washington, United States of America, IEEE. 17.Google Scholar
Kujala, P., Hänninen, M., Arola, T. and Ylitalo, J. (2009). Analysis of the marine traffic safety in the Gulf of Finland. Reliability Engineering & System Safety, 94(8), 13491357.Google Scholar
Li, Y., Liu, R. W., Liu, J., Huang, Y., Hu, B. and Wang, K. (2016). Trajectory compression-guided visualization of spatio-temporal AIS vessel density. 8th International Conference in Wireless Communications & Signal Processing (WCSP16), Yangzhou, Jiangsu, China. IEEE. 15.Google Scholar
Mazzarella, F., Vespe, M., Damalas, D. and Osio, G. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. 17th International Conference on Information Fusion (FUSION). Salamanca, Spain. IEEE. 17.Google Scholar
Meratnia, N., & Rolf, A. (2004). Spatiotemporal compression techniques for moving point objects. In International Conference on Extending Database Technology, Berlin, Germany. Springer.Google Scholar
Muthu, S. S. (2015). Visualization, statistical analysis, and mining of historical vessel data. Doctoral dissertation, University of New Brunswick.Google Scholar
Olindersson, F. and Janson, C. (2015). Development of a software to identify and analyse marine traffic situations. International Conference on Marine Simulation and Ship Manoeuvrability (MARSIM), Newcastle, United Kingdom.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 22182245.Google Scholar
Patroumpas, K., Artikis, A., Katzouris, N., Vodas, M., Theodoridis, Y. and Pelekis, N. (2015). Event Recognition for Maritime Surveillance. 18th International Conference on Extending Database Technology (EDBT), Brussels, Belgium. 629640.Google Scholar
Qi, L. and Zheng, Z. (2016). A measure of similarity between trajectories of vessels. Journal of Engineering Science and Technology Review, 9(1), 1722.Google Scholar
Rong, H. and Mou, J. (2013). Predict maneuvering indices using AIS data by ridge regression. International Workshop on Next Generation Nautical Traffic Models, Delft, The Netherlands. 102111.Google Scholar
Sun, P., Xia, S., Yuan, G. and Li, D. (2016). An Overview of Moving Object Trajectory Compression Algorithms. Mathematical Problems in Engineering, Article ID 6587309.Google Scholar
Wang, Y., Zhang, J., Chen, X., Chu, X. and Yan, X. (2013). A spatial–temporal forensic analysis for inland–water ship collisions using AIS data. Safety Science, 57, 187202.Google Scholar
Willems, N., Van De Wetering, H. and Van Wijk, J. J. (2009). Visualization of vessel movements. Computer Graphics Forum, 28(3), 959966.Google Scholar
Wu, X., Mehta, A. L., Zaloom, V. A. and Craig, B. N. (2016). Analysis of waterway transportation in Southeast Texas waterway based on AIS data. Ocean Engineering, 121, 196209.Google Scholar
Zhang, S. K., Liu, Z. J., Cai, Y., Wu, Z. L. and Shi, G. Y. (2016). AIS trajectories simplification and threshold determination. The Journal of Navigation, 69(4), 729744.Google Scholar
Zhang, S. K., Shi, G. Y., Liu, Z. J., Zhao, Z. W. and Wu, Z. L. (2018). Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity. Ocean Engineering, 155, 240250.Google Scholar