Skip to main content
SpringerLink
Log in

A Study of Vessel Trajectory Compression Based on Vector Data Compression Algorithms

  • Conference paper
  • First Online:
Business Information Systems Workshops (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 373))

Included in the following conference series:

Abstract

With the development of information technology and its vast applications in vessel traffic, such as the popular Automatic Identification System (AIS), a large quantity of vessel trajectory data has been recorded and stored. Vessel traffic has also entered the age of big data. However, the redundancy of data considerably reduces the availability of research and applications, and how to compress these data becomes a problem that needs to be solved. In this paper, several classical vector data compression algorithms are summarized, and the ideas of each algorithm and the steps to compress vessel trajectories are introduced. The vessel trajectory compression experiments based on the algorithms are performed. The results are analyzed, and the characteristics of each algorithm are summarized. The results and conclusions lay the foundation for the selection and improvement of the algorithms in vessel trajectory compression. Through the study of this paper, a systematic theoretical support for the compression of vessel trajectories is provided, which could guide practical applications.

Supported by “the Fundamental Research Funds for the Central Universities” (No. 3132016021).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bell, M.G., Meng, Q.: Special issue in transportation research part b-shipping, port and maritime logistics. Transp. Res. Part B: Methodol. 93(PB), 697–699 (2016). https://doi.org/10.1016/j.trb.2016.09.003

    Article  Google Scholar 

  2. Bole, A.G., Dineley, W.O., Wall, A.: Chapter 5 - automatic identification system (AIS). In: Radar and ARPA Manual, Oxford, 3rd edn, pp. 255–275 (2014). https://doi.org/10.1016/B978-0-08-097752-2.00005-2

  3. Borkowski, P.: The ship movement trajectory prediction algorithm using navigational data fusion. Sensors 17(6), 1432 (2017). https://doi.org/10.3390/s17061432

    Article  Google Scholar 

  4. Chen, F., Ren, H.: Comparison of vector data compression algorithms in mobile GIS. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 1, pp. 613–617 (2010). https://doi.org/10.1109/ICCSIT.2010.5564118

  5. Clements, J.C.: The optimal control of collision avoidance trajectories in air traffic management. Transp. Res. Part B: Methodol. 33(4), 265–280 (1999). https://doi.org/10.1016/S0191-2615(98)00031-9

    Article  Google Scholar 

  6. Dittmar, C.: Die nächste evolutionsstufe von AIS: big data. In: Gluchowski, P., Chamoni, P. (eds.) Analytische Informationssysteme, pp. 55–65. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-47763-2_4

    Chapter  Google Scholar 

  7. Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., Wolle, T.: Compressing spatio-temporal trajectories. Comput. Geom. 42(9), 825–841 (2009). https://doi.org/10.1016/j.comgeo.2009.02.002

    Article  MathSciNet  MATH  Google Scholar 

  8. Ifrim, C., Iuga, I., Pop, F., Wallace, M., Poulopoulos, V.: Data reduction techniques applied on automatic identification system data. In: International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, pp. 14–19 (2017). https://doi.org/10.1007/978-3-319-74497-12

  9. Isenor, A.W., St-Hilaire, M.O., Webb, S., Mayrand, M.: MSARI: a database for large volume storage and utilisation of maritime data. J. Navig. 70(2), 276–290 (2017). https://doi.org/10.1017/S0373463316000540

    Article  Google Scholar 

  10. Ji, H., Wang, Y.: The research on the compression algorithms for vector data. In: 2010 International Conference on Multimedia Technology, pp. 1–4 (2010). https://doi.org/10.1109/ICMULT.2010.5631153

  11. Lever, R., Hinze, A., Buchanan, G.: Compressing GPS data on mobile devices. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006. LNCS, vol. 4278, pp. 1944–1947. Springer, Heidelberg (2006). https://doi.org/10.1007/11915072_102

    Chapter  Google Scholar 

  12. Li, Y., Zhong, E.: A new vector data compression approach for WebGIS. Geo-Spat. Inf. Sci. 14(1), 48–53 (2011). https://doi.org/10.1007/s11806-011-0431-1

    Article  Google Scholar 

  13. Mao, S., Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., Huang, G.B.: An automatic identification system (AIS) database for maritime trajectory prediction and data mining. In: Proceedings of ELM-2016, pp. 241–257 (2018). https://doi.org/10.1007/978-3-319-57421-9_20

  14. Moffitt, K.C., Vasarhelyi, M.A.: AIS in an age of big data. J. Inf. Syst. 27(2), 1–19 (2013). https://doi.org/10.2308/isys-10372

    Article  Google Scholar 

  15. Montanino, M., Punzo, V.: Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns. Transp. Res. Part B: Methodol. 80, 82–106 (2015). https://doi.org/10.1016/j.trb.2015.06.010

    Article  Google Scholar 

  16. Popa, I.S., Zeitouni, K., Oria, V., Kharrat, A.: Spatio-temporal compression of trajectories in road networks. GeoInformatica 19(1), 117–145 (2015). https://doi.org/10.1007/s10707-014-0208-4

    Article  Google Scholar 

  17. Tichavska, M., Cabrera, F., Tovar, B., Araña, V.: Use of the automatic identification system in academic research. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2015. LNCS, vol. 9520, pp. 33–40. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27340-2_5

    Chapter  Google Scholar 

  18. de Vries, G., van Someren, M.: Clustering vessel trajectories with alignment kernels under trajectory compression. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 296–311. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15880-3_25

    Chapter  Google Scholar 

  19. Wang, P., Goverde, R.M.: Multi-train trajectory optimization for energy efficiency and delay recovery on single-track railway lines. Transp. Res. Part B: Methodol. 105, 340–361 (2017). https://doi.org/10.1016/j.trb.2017.09.012

    Article  Google Scholar 

  20. Wu, X., Mehta, A.L., Zaloom, V.A., Craig, B.N.: Analysis of waterway transportation in Southeast Texas waterway based on AIS data. Ocean Eng. 121, 196–209 (2016). https://doi.org/10.1016/j.oceaneng.2016.05.012

    Article  Google Scholar 

  21. Wu, X., Rahman, A., Zaloom, V.A.: Study of travel behavior of vessels in narrow waterways using AIS data-a case study in Sabine-Neches waterways. Ocean Eng. 147, 399–413 (2018). https://doi.org/10.1016/j.oceaneng.2017.10.049

    Article  Google Scholar 

  22. Zhang, L., Meng, Q., Fwa, T.F.: Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transp. Res. Part E: Logist. Transp. Rev. (2017). https://doi.org/10.1016/j.tre.2017.07.011

    Article  Google Scholar 

  23. Zhang, L., Meng, Q., Xiao, Z., Fu, X.: A novel ship trajectory reconstruction approach using AIS data. Ocean Eng. 159, 165–174 (2018). https://doi.org/10.1016/j.oceaneng.2018.03.085

    Article  Google Scholar 

  24. Zhang, S., Liu, Z., Cai, Y., Wu, Z., Shi, G.: AIS trajectories simplification and threshold determination. J. Navig. 69(4), 729–744 (2016)

    Article  Google Scholar 

  25. Zhang, S., Shi, G., Liu, Z., Zhao, Z., Wu, Z.: Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity. Ocean Eng. 155, 240–250 (2018). https://doi.org/10.1016/j.oceaneng.2018.02.060

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by “the Fundamental Research Funds for the Central Universities” (No. 3132016021). The authors thank the researchers who participated in the data processing and provided language assistance.

Author information

Authors and Affiliations

  1. Information Science Technology College, Dalian Maritime University, Dalian, 116026, China

    Yuanyuan Ji, Wenhai Xu & Ansheng Deng

Authors
  1. Yuanyuan Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenhai Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ansheng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenhai Xu .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. University of Seville, Seville, Spain

    Rafael Corchuelo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ji, Y., Xu, W., Deng, A. (2019). A Study of Vessel Trajectory Compression Based on Vector Data Compression Algorithms. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-36691-9_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36691-9_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36690-2

  • Online ISBN: 978-3-030-36691-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation