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Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering

Published online by Cambridge University Press:  03 July 2017

Jiang Wang ,
Cheng Zhu ,
Yun Zhou  and
Weiming Zhang
Jiang Wang
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Cheng Zhu*
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Yun Zhou
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
Weiming Zhang
Affiliation:
(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, P. R. China)
*
(E-mail: [email protected])
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Abstract

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

Keywords

Co-clusteringTrajectoriesSpatio-temporal Data MiningAIS Data
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1383 - 1400
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Aarsaether, K.G. and Moan, T. (2009). Estimating Navigation Patterns from AIS. The Journal of Navigation, 62(4), 587–607.CrossRefGoogle Scholar
Breithaupt, S.A., Copping, A., Tagestad, J. and Whiting, J. (2017). Maritime Route Delineation using AIS Data from the Atlantic Coast of the US. The Journal of Navigation, 70(2), 379–394.CrossRefGoogle Scholar
Chen, J., Lu, F. and Peng, G. (2015). A Quantitative Approach for Delineating Principal Fairways of Ship Passages through a Strait. Ocean Engineering, 103, 188–197.Google Scholar
George, J., Crassidis, J., Singh, T. and Fosbury, A.M. (2011). Anomaly Detection using Context-Aided Target Tracking. Journal of Advances in Information Fusion, 6(1), 39–56.Google Scholar
Giannotti, F., Nanni, M., Pinelli, F. and Pedreschi, D. (2007). Trajectory Pattern Mining. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 330–339, ACM.Google Scholar
Goerlandt, F., and Kujala, P. (2011). Traffic Simulation Based Ship Collision Probability Modeling. Reliability Engineering & System Safety, 96(1), 91–107.Google Scholar
Goerlandt, F., Goite, H., Valdez Banda, O.A., Höglund, A., Ahonen-Rainio, P. and Lensu, M. (2017). An Analysis of Wintertime Navigational Accidents in the Northern Baltic Sea. Safety Science, 92, 66–84.Google Scholar
Goerlandt, F., Montewka, J., Zhang, W. and Kujala, P. (2017). An Analysis of Ship Escort and Convoy Operations in Ice Conditions. Safety Science, 95, 198–209.Google Scholar
Hansen, M.G., Jensen, T.K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F.M. and Ennemark, F. (2013). Empirical Ship Domain Based on AIS Data. The Journal of Navigation, 66, 931–940.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), 373–389.Google Scholar
Johansson, F. and Falkman, G. (2007). Detection of Vessel Anomalies - A Bayesian Network Approach. Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, 395–400, IEEE.Google Scholar
Lane, R.O., Nevell, D.A., Hayward, S.D. and Beaney, T.W. (2010). Maritime Anomaly Detection and Threat Assessment. Proceedings of the 13th International Conference on Information Fusion, FUSION'10, pp. 1–8, IEEE, Edinburgh, UK.Google Scholar
Laws, K., Vesecky, J. and Paduan, J. (2011). Monitoring Coastal Vessels for Environmental Applications: Application of Kalman Filtering. Proceedings of IEEE/OES 10th Current, Waves and Turbulence Measurements (CWTM), 39–46, Monterey, California, USA.Google Scholar
Laxhammar, R. (2008). Anomaly Detection for Sea Surveillance. Proceedings of the 11th International Conference on Information Fusion, FUSION'08, 1–8, IEEE, Cologne, Germany.Google Scholar
Laxhammar, R. and Falkman, G. (2010). Conformal Prediction for Distribution-independent Anomaly Detection In Streaming Vessel Data. Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, 47–55, ACM, Washington, DC, USA.Google Scholar
Laxhammar, R., Falkman, G. and Sviestins, E. (2009). Anomaly Detection in Sea Traffic - A Comparison of the Gaussian Mixture Model and the Kernel Density Estimator. Proceedings of the 12th International Conference on Information Fusion, FUSION'09, 756–763, IEEE, Seattle, WA, USA.Google Scholar
Li, X., Han, J. and Kim, S. (2006). Motion-alert: Automatic Anomaly Detection in Massive Moving Objects. Proceedings of IEEE Intelligence and Security Informatics, 166–177, San Diego, California, USA.CrossRefGoogle Scholar
Liu, C. and Chen, X. (2014). Vessel Track Recovery with Incomplete AIS Data using Tensor CANDECOM/PARAFAC Decomposition. The Journal of Navigation, 67(1), 83–99.CrossRefGoogle Scholar
Mascaro, S., Nicholso, A.E. and Korb, K.B. (2014). Anomaly Detection in Vessel Tracks using Bayesian Networks. International Journal of Approximate Reasoning, 55(1), 84–98.Google Scholar
Montewka, J., Goerlandt, F., Kujala, P. and Lensu, M. (2015). Towards Probabilistic Models for the Prediction of a Ship Performance in Dynamic Ice. Cold Regions Science and Technology, 112, 14–28.CrossRefGoogle Scholar
Oliva, J.B. (2012). Anomaly Detection and Modeling of Trajectories. (No. CMU-CS-12-133). Carnegie-Mellon University Pittsburgh PA School of Computer Science.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), 2218–2245.Google Scholar
Pan, J., Jiang, Q., Hu, J. and Shao, Z. (2012). An AIS Data Visualization Model for Assessing Maritime Traffic Situation and Its Applications. Procedia Engineering, 29, 365–369.Google Scholar
Papalexakis, E.E., Sidiropoulos, N.D. and Bro, R. (2013). From K-means to Higher-way Co-clustering: Multilinear Decomposition with Sparse Latent Factors. IEEE Transactions on Signal Processing, 61(2), 493–506.Google Scholar
Rhodes, B.J., Bomberger, N.A. and Zandipour, M. (2007). Probabilistic Associative Learning of Vessel Motion Patterns at Multiple Spatial Scales for Maritime Situation Awareness. Proceedings of the 10th International Conference on Information Fusion, 1–8, Quebec, Canada.Google 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. Proceedings of the 11th International Conference on Information Fusion, FUSION'08, 1–7, IEEE, Cologne, Germany.Google Scholar
Rong, H. and Mou, J-M. (2013). Predict Maneuvering Indices using AIS Data by Ridge Regression. International Workshop on Next Generation Nautical Traffic Models, Delft, The Netherlands, 102–111.Google Scholar
Shapiro, L. and Stockman, G.C. (2001). Computer Vision. Prentice Hall.Google Scholar
Tun, M.H., Chambers, G.S., Tan, T. and Ly, T. (2007). Maritime Port Intelligence using AIS Data. Recent Advances in Security Technology, Proceedings of the 2007 RNSA Security technology Conference, Melbourne, Australia, 33–43.Google Scholar
Van Westrenen, F. and Ellerbroek, J. (2017). The Effect of Traffic Complexity on the Development of Near Misses on the North Sea. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(3), 432–440.Google Scholar
Vespe, M., Sciotti, M., Burro, F., Battistello, G. and Sorge, S. (2008). Maritime Multi-sensor Data Association Based on Geographic and Navigational Knowledge. Proceedings of IEEE Radar Conference RADAR 08, 1–6, Rome, Italy.Google Scholar
Wang, Y. and Chin, H.-C. (2016). An Empirically-calibrated Ship Domain as a Safety Criterion for Navigation in Confined Waters. The Journal of Navigation, 69, 257–276.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, 187–202.CrossRefGoogle Scholar
Will, J., Peel, L. and Claxton, C. (2011). Fast Maritime Anomaly Detection using KD-tree Gaussian Processes. Proceedings of the 2nd IMA Conference on Maths in Defence, Shrivenham, UK (Vol. 20).Google Scholar
Willems, N., Van De Wetering, H., and Van Wijk, J.J. (2009). Visualization of Vessel Movements. Computer Graphics Forum, 28(3), 959–966. Blackwell Publishing Ltd.CrossRefGoogle Scholar
Zhang, W., Goerlandt, F., Kujala, P. and Wang, Y. (2016). An Advanced Method for Detecting Possible Near Miss Ship Collisions from AIS Data. Ocean Engineering, 124, 141–156.Google Scholar