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Vessel Trajectory Online Multi-Dimensional Simplification Algorithm

  • Yuan-qiang Zhang (a1) (a2), Guo-you Shi (a1), Song Li (a2) and Shu-kai Zhang (a3)


Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course.


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Aarsæther, K.G. and Moan, T. (2009). Estimating Navigation Patterns from AIS. The Journal of Navigation, 62, 587607.
Altan, Y.C. and Otay, E.N. (2017). Maritime Traffic Analysis of the Strait of Istanbul based on AIS data. The Journal of Navigation, 70, 13671382.
Bertolotto, M. and Zhou, M. (2007). Efficient and consistent line simplification for web mapping. International Journal of Web Engineering and Technology, 3, 139156.
Cao, W. and Li, Y. (2017). DOTS: An online and near-optimal trajectory simplification algorithm. Journal of Systems and Software, 126, 3444.
Chen, C.J., Lee, T.Y., Huang, Y.M. and Lai, F.J. (2009). Extraction of characteristic points and its fractal Reconstruction for terrain profile data. Chaos Solutions & Fractals, 39, 17321743.
Chen, M., Xu, M. and Fränti, P. (2012). A fast O(N) Multiresolution Polygonal Approximation Algorithm for GPS Trajectory Simplification. IEEE Transactions on Image Processing, 21, 27702785.
Deng, Z., Han, W., Wang, L., Ranjan, R., Zomaya, A.Y. and Jie, W. (2017). An efficient online direction-preserving compression approach for trajectory streaming data. Future Generation Computer Systems, 68, 150162.
Douglas, D. and Peucker, T. (1973). Algorithm for the reduction of the number of points required to represent a digital line or its caricature. Journal of the Canadian Cartographer, 10, 112–22.
Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C. and Wolle, T. (2009). Compressing spatio-temporal trajectories. Computational Geometry: Theory and Applications, 42, 825841.
International Telecommunication Union (ITU). (2014). Technical Characteristics for an Automatic Identification System Using Time-Division Multiple Access in the VHF Maritime Mobile Band. Accessed February 2014.
International Maritime Organization (IMO). (2014). International Convention for the Safety of Life at Sea (SOLAS). China Communications Press Co., Ltd.
Keogh, E., Chu, S., Hart, D. and Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. IEEE International Conference on Data Mining, 289, 289296.
Ke, B., Shao, J. and Zhang, D. (2017). An Efficient Online Approach for Direction-Preserving Trajectory Simplification with Interval Bounds. IEEE International Conference on Mobile Data Management, 5055.
Liu, G., Iwai, M. and Sezaki, K. (2013). An Online Method for Trajectory Simplification Under Uncertainty of GPS. Information and Media Technologies, 6, 665674.
Long, C., Wong, R.C.-W. and Jagadish, H.V. (2013). Direction-preserving trajectory simplification. VLDB Endowment, 6, 949960.
Long, C., Wong, R.C.-W. and Jagadish, H.V. (2015). Trajectory Simplification: On Minimizing the Direction-based Error. VLDB Endowment, 8, 4960.
Mazaheri, A., Montewka, J., Kotilainen, P., Sormunen, O.-V.E. and Kujala, P. (2015). Assessing Grounding Frequency using Ship Traffic and Waterway Complexity. The Journal of Navigation, 68, 89106.
Meratnia, N. and By, R.A.D. (2004). Spatiotemporal Compression Techniques for Moving Point Objects. International Conference on Advances in Database Technology-EDBT, 2992, 765782.
Meng, Q., Yu, X., Yao, C. and Li, X. (2017). Improvement of OPW-TR Algorithm for Compressing GPS Trajectory Data. Journal of Information Processing Systems, 13, 533545.
Muckell, J., Olsen, P.W. Jr, Hwang, J.-H., Lawson, C.T. and Ravi, S.S. (2013). Compression of trajectory data: a comprehensive evaluation and new approach. Geoinformatica, 18, 435460.
Pallero, J.L.G. (2013). Robust line simplification on the plane. Computers & Geosciences, 61, 152159.
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, 22182245.
Potamias, M., Patroumpas, K. and Sellis, T. (2006). Sampling Trajectory Streams with Spatiotemporal Criteria. International Conference on Scientific & Statistical Database Management, 52, 275284.
Ristic, B., Scala, B.L., Morelande, M. and Gordon, N. (2008). Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction. International Conference on Information Fusion, 29, 17.
Shi, S. and Charlton, M. (2013). A new approach and procedure for generalising vector-based maps of realworld features. GIScience & Remote Sensing, 50, 473482.
Shu, Y.Q., Daamen, D., Ligteringen, H. and Hoogendoorn, S. (2013). Vessel Speed, Course, and Path Analysis in the Botlek Area of the Port of Rotterdam, Netherlands. Transportation Research Record Journal of the Transportation Research Board, 2330, 6372.
Sidibé, A. and Shu, G. (2017). Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels. The Journal of Navigation, 70, 847858.
Silveira, P.A.M., Teixeira, A.P. and Soares, G.C. (2013). Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. The Journal of Navigation, 66, 879898.
Vries, G.K,D.D. and Someren, M.V. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications, 39, 1342613439.
Wu, F., Fu, K., Wang, Y. and Xiao, Z. (2017a). A Graph-Based Min-# and Error-Optimal Trajectory Simplification Algorithm and Its Extension towards Online Services. International Journal of Geo-Information, 6, 121.
Wu, L., Xu, Y.J., Wang, Q., Wang, F. and Xu, Z.W. (2017b). Mapping Global Shipping Density from AIS Data. The Journal of Navigation, 70, 6781.
Wang, J., Zhu, C., Zhou, Y. and Zhang, W. (2017). Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering. The Journal of Navigation, 70, 13831400.
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.
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, 729744.
Zhang, S.K., Liu, Z.J., Zhang, X.H., Shi, G.Y. and Cai, Y. (2015). A method for AIS track data compression based on Douglas-Peucker algorithm. Journal of Harbin Engineering University, 36, 595599. (in Chinese)
Zheng, Y. (2015). Trajectory Data Mining: An Overview. ACM, 6, 141.
Zhu, F.X., Miao, L.M. and Liu, W. (2014). Research on Vessel Trajectory Multi-Dimensional Compression Algorithm Based on Douglas-Peucker Theory. Applied Mechanics and Materials, 694, 5962.



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