Vessel track information mining using AIS data

In recent years, vessel traffic and maritime situation awareness become more and more important for countries across the world. AIS data contains much information about vessel motion and reflects traffic characteristics. In this paper, data mining is introduced to discover motion patterns of vessel movements. Firstly, we do statistical analysis for large scale of AIS data. Secondly, we use association rules to analyze the frequent moving status of vessels. We extend the dimensions of data features, improve the algorithm in efficiency and import the concept of time scale in the algorithm based on the previous relative work. Thirdly, we introduce Markov model to make supplement for the association rules. The prediction results in the Markov process are further used to do the anomaly detection. The method in this paper provides novel idea for the research in AIS data and the management of maritime traffic.

[1]  Thad Starner,et al.  Learning Significant Locations and Predicting User Movement with GPS , 2002, Proceedings. Sixth International Symposium on Wearable Computers,.

[2]  Kevin B. Korb,et al.  Anomaly detection in vessel tracks using Bayesian networks , 2014, Int. J. Approx. Reason..

[3]  Lars Niklasson,et al.  Trajectory clustering for coastal surveillance , 2007, 2007 10th International Conference on Information Fusion.

[4]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[5]  Yoshihide Sekimoto,et al.  Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform , 2013, UbiComp.

[6]  Göran Falkman,et al.  The role of visualization and interaction in maritime anomaly detection , 2011, Electronic Imaging.

[7]  James B. Kraiman,et al.  Automated anomaly detection processor , 2002, SPIE Defense + Commercial Sensing.

[8]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[9]  Feixiang Zhu,et al.  Mining ship spatial trajectory patterns from AIS database for maritime surveillance , 2011, 2011 2nd IEEE International Conference on Emergency Management and Management Sciences.