Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-27T18:56:20.688Z Has data issue: false hasContentIssue false
  • English
  • Français

Vessel Trajectory Online Multi-Dimensional Simplification Algorithm

Published online by Cambridge University Press:  22 August 2019

Yuan-qiang Zhang ,
Guo-you Shi ,
Song Li  and
Shu-kai Zhang
Yuan-qiang Zhang
Affiliation:
(Navigation College, Dalian Maritime University, Dalian116026, China) (Faculty of Maritime and Transportation, Ningbo University, Ningbo315211, China)
Guo-you Shi*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian116026, China)
Song Li
Affiliation:
(Faculty of Maritime and Transportation, Ningbo University, Ningbo315211, China)
Shu-kai Zhang
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

AIS TrajectoriesTrajectory simplificationOnline algorithmDOTS algorithm
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 2 , March 2020 , pp. 342 - 363
Copyright
Copyright © The Royal Institute of Navigation 2019

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

REFERENCES

Aarsæther, K.G. and Moan, T. (2009). Estimating Navigation Patterns from AIS. The Journal of Navigation, 62, 587–607.CrossRefGoogle Scholar
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, 1367–1382.CrossRefGoogle Scholar
Bertolotto, M. and Zhou, M. (2007). Efficient and consistent line simplification for web mapping. International Journal of Web Engineering and Technology, 3, 139–156.CrossRefGoogle Scholar
Cao, W. and Li, Y. (2017). DOTS: An online and near-optimal trajectory simplification algorithm. Journal of Systems and Software, 126, 34–44.CrossRefGoogle Scholar
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, 1732–1743.CrossRefGoogle Scholar
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, 2770–2785.CrossRefGoogle Scholar
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, 150–162.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C. and Wolle, T. (2009). Compressing spatio-temporal trajectories. Computational Geometry: Theory and Applications, 42, 825–841.CrossRefGoogle Scholar
International Telecommunication Union (ITU). (2014). Technical Characteristics for an Automatic Identification System Using Time-Division Multiple Access in the VHF Maritime Mobile Band. http://www.itu.int/rec/R-REC-M.1371/en. Accessed February 2014.Google Scholar
International Maritime Organization (IMO). (2014). International Convention for the Safety of Life at Sea (SOLAS). China Communications Press Co., Ltd.Google Scholar
Keogh, E., Chu, S., Hart, D. and Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. IEEE International Conference on Data Mining, 289, 289–296.Google Scholar
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, 50–55.Google Scholar
Liu, G., Iwai, M. and Sezaki, K. (2013). An Online Method for Trajectory Simplification Under Uncertainty of GPS. Information and Media Technologies, 6, 665–674.Google Scholar
Long, C., Wong, R.C.-W. and Jagadish, H.V. (2013). Direction-preserving trajectory simplification. VLDB Endowment, 6, 949–960.CrossRefGoogle Scholar
Long, C., Wong, R.C.-W. and Jagadish, H.V. (2015). Trajectory Simplification: On Minimizing the Direction-based Error. VLDB Endowment, 8, 49–60.CrossRefGoogle Scholar
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, 89–106.CrossRefGoogle Scholar
Meratnia, N. and By, R.A.D. (2004). Spatiotemporal Compression Techniques for Moving Point Objects. International Conference on Advances in Database Technology-EDBT, 2992, 765–782.Google Scholar
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, 533–545.Google Scholar
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, 435–460.CrossRefGoogle Scholar
Pallero, J.L.G. (2013). Robust line simplification on the plane. Computers & Geosciences, 61, 152–159.CrossRefGoogle 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, 2218–2245.CrossRefGoogle Scholar
Potamias, M., Patroumpas, K. and Sellis, T. (2006). Sampling Trajectory Streams with Spatiotemporal Criteria. International Conference on Scientific & Statistical Database Management, 52, 275–284.Google Scholar
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, 1–7.Google Scholar
Shi, S. and Charlton, M. (2013). A new approach and procedure for generalising vector-based maps of realworld features. GIScience & Remote Sensing, 50, 473–482.CrossRefGoogle Scholar
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, 63–72.CrossRefGoogle Scholar
Sidibé, A. and Shu, G. (2017). Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels. The Journal of Navigation, 70, 847–858.CrossRefGoogle Scholar
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, 879–898.CrossRefGoogle Scholar
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, 13426–13439.CrossRefGoogle Scholar
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, 1–21.Google Scholar
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, 67–81.CrossRefGoogle Scholar
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, 1383–1400.CrossRefGoogle 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, 240–250.CrossRefGoogle 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, 729–744.CrossRefGoogle Scholar
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, 595–599. (in Chinese)Google Scholar
Zheng, Y. (2015). Trajectory Data Mining: An Overview. ACM, 6, 1–41.Google Scholar
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, 59–62.CrossRefGoogle Scholar