Skip to main content

Optimizing vessel trajectory compression for maritime situational awareness

GeoInformatica (2022)Cite this article

Abstract

We present an open-source system that can optimize compressed trajectory representations for large fleets of vessels. We take into account the type of each vessel in order to choose a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. We employ a genetic algorithm that converges to a fine-tuned configuration per vessel type without any hyper-parameter tuning. These configurations can provide synopses that retain less than 10% of the original points with less than 20m approximation error in a real world dataset; in another dataset with 90% less samples than the previous one, the synopses retain 20% of the points and achieve less than 80m error. Additionally the level of compression can be chosen by the user, by setting the desired approximation error. Our system also supports incremental optimization by training in data batches, and therefore continuously improves performance. Furthermore, we employ a composite event recognition engine to efficiently detect complex maritime activities, such as ship-to-ship transfer and loitering; thanks to the synopses generated by the genetic algorithm instead of the raw trajectories, we make the recognition process faster while also maintaining the same level of recognition accuracy. Our extensive empirical study demonstrates the effectiveness of our system over large, real-world datasets.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Data availability

The BR dataset analyzed during the current study is available in [36], https://doi.org/10.5281/zenodo.1167595. The MS dataset was given to us by MarineTraffic, our partner in the INFORE project, and restrictions apply to the availability of these data, and so are not publicly available. Finally, the sources for data used for composite maritime event recognition are summarized in Table 5.

Notes

  1. https://www.marinetraffic.com/

  2. https://github.com/DataStories-UniPi/Trajectory-Synopses-Generator

  3. https://github.com/aartikis/RTEC

  4. https://github.com/GiannisFikioris/Genetic-Algorithm-for-Synopses-Generator

  5. https://deap.readthedocs.io

  6. https://flink.apache.org/

  7. https://en.wikipedia.org/wiki/YAP_(Prolog)

  8. https://www.swi-prolog.org/

  9. https://ec.europa.eu/environment/nature/natura2000/index_en.htm

References

  1. Agarwal PK, Har-Peled S, Mustafa NH, Wang Y (2002) Near-linear time approximation algorithms for curve simplification. In: ESA. pp 29–41

  2. Alevizos E, Artikis A, Paliouras G (2017) Event forecasting with pattern Markov chains. In: DEBS. pp 146–157

  3. Alevizos E, Skarlatidis A, Artikis A, Paliouras G (2017) Probabilistic complex event recognition: a survey. ACM Comput Surv 50(5):71:1–71:31

  4. Arasteh S, Tayebi MA, Zohrevand Z, Glässer U, Shahir AY, Saeedi P, Wehn H (2020) Fishing vessels activity detection from longitudinal AIS data. In: SIGSPATIAL. pp 347–356

  5. Artikis A, Sergot MJ, Paliouras G (2015) An event calculus for event recognition. IEEE Trans Knowl Data Eng 27(4):895–908

    Article  Google Scholar 

  6. Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228

    Article  Google Scholar 

  7. Cugola, G, Margara A (2012) Processing flows of information: from data stream to complex event processing. ACM Comput Surv 44(3):15:1–15:62

  8. datAcron H2020 ICT-16 Project. https://www.iit.demokritos.gr/projects/datacron/. Accessed 26 Aug 2022

  9. Douglas D, Peucker T (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can Cartogr 10(2):112–122

    Article  Google Scholar 

  10. European Environment Agency: Europe coastline shapefile (2013). https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile. Accessed 26 Aug 2022

  11. Fikioris G, Patroumpas K, Artikis A (2020) Optimizing vessel trajectory compression. In: MDM. pp 281–286

  12. Fikioris G, Patroumpas K, Artikis A, Paliouras G, Pitsikalis M (2020) Fine-tuned compressed representations of vessel trajectories. In: CIKM. pp 2429–2436

  13. Giatrakos N, Alevizos E, Artikis A, Deligiannakis A, Garofalakis MN (2020) Complex event recognition in the big data era: a survey. VLDB J 29(1):313–352

    Article  Google Scholar 

  14. Grez A, Riveros C, Ugarte M (2019) A formal framework for complex event processing. In: ICDT, vol. 127. LIPIcs, pp 5:1–5:18

  15. Iphar C, Napoli A, Ray C (2015) Detection of false AIS messages for the improvement of maritime situational awareness. In: OCEANS. pp 1–7

  16. Katzouris N, Artikis A (2020) WOLED: a tool for online learning weighted answer set rules for temporal reasoning under uncertainty. In: KR. pp 790–799

  17. Kontopoulos I, Chatzikokolakis K, Tserpes K, Zissis D (2020) Classification of vessel activity in streaming data. In: DEBS. pp 153–164

  18. Lange R, Dürr F, Rothermel K (2011) Efficient real-time trajectory tracking. VLDB J 20(5):671–694

    Article  Google Scholar 

  19. Lin X, Jiang J, Ma S, Zuo Y, Hu C (2019) One-pass trajectory simplification using the synchronous Euclidean distance. VLDB J 28(6):897–921

    Article  Google Scholar 

  20. Lin X, Ma S, Zhang H, Wo T, Huai J (2017) One-pass error bounded trajectory simplification. PVLDB 10(7):841–852

    Google Scholar 

  21. Liu J, Zhao K, Sommer P, Shang S, Kusy B, Jurdak R (2015) Bounded quadrant system: Error-bounded trajectory compression on the go. In: ICDE. pp 987–998

  22. Liu J, Zhao K, Sommer P, Shang S, Kusy B, Lee J, Jurdak R (2016) A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE Trans Knowl Data Eng 28(11):2827–2841

    Article  Google Scholar 

  23. Ljunggren H (2018) Using deep learning for classifying ship trajectories. In: FUSION. pp 2158–2164

  24. Long C, Wong RCW, Jagadish H (2014) Trajectory simplification: on minimizing the direction-based error. PVLDB 8(1):49–60

    Google Scholar 

  25. Makris A, Kontopoulos I, Alimisis P, Tserpes K (2021) A comparison of trajectory compression algorithms over AIS data. IEEE Access 9:92516–92530

    Article  Google Scholar 

  26. Meratnia N, deBy R (2004) Spatiotemporal compression techniques for moving point objects. In: EDBT. pp 765–782

  27. Muckell J, Olsen P, Hwang JH, Lawson C, Ravi S (2014) Compression of trajectory data: a comprehensive evaluation and new approach. GeoInformatica 18(3):435–460

    Article  Google Scholar 

  28. Natale F, Gibin M, Alessandrini A, Vespe M, Paulrud A (2015) Mapping fishing effort through AIS data. PLoS ONE 10(6):1–16

    Article  Google Scholar 

  29. Nguyen D, Vadaine R, Hajduch G, Garello R, Fablet R (2018) A multi-task deep learning architecture for maritime surveillance using AIS data streams. In: DSAA. pp 331–340

  30. Patroumpas K (2021) Online mobility tracking against evolving maritime trajectories. In: Artikis A, Zissis D (eds) Guide to Maritime Informatics. Springer

  31. Patroumpas K, Alevizos E, Artikis A, Vodas M, Pelekis N, Theodoridis Y (2017) Online event recognition from moving vessel trajectories. GeoInformatica 21(2):389–427

    Article  Google Scholar 

  32. Patroumpas K, Pelekis N, Theodoridis Y (2018) On-the-fly mobility event detection over aircraft trajectories. In: ACM SIGSPATIAL. pp 259–268

  33. Pitsikalis M, Artikis A, Dreo R, Ray C, Camossi E, Jousselme A (2019) Composite event recognition for maritime monitoring. In: DEBS. pp 163–174

  34. Potamias M, Patroumpas K, Sellis T (2006) Sampling trajectory streams with spatiotemporal criteria. In: SSDBM. pp 275–284

  35. Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: International Symposium on Spatial and Temporal Databases. Springer, pp 148–166

  36. Ray C, Dréo R, Camossi E, Jousselme AL, Iphar C (2019) Heterogeneous integrated dataset for maritime intelligence, surveillance, and reconnaissance. Data in Brief 21. https://doi.org/10.5281/zenodo.1167595

  37. Santipantakis GM, Vlachou A, Doulkeridis C, Artikis A, Kontopoulos I, Vouros GA (2018) A stream reasoning system for maritime monitoring. In: TIME. pp 20:1–20:17

  38. Snidaro L, Visentini I, Bryan K, Foresti GL (2012) Markov Logic Networks for context integration and situation assessment in maritime domain. In: FUSION. IEEE, pp 1534–1539

  39. Terroso-Saenz F, Valdés-Vela M, den Breejen E, Hanckmann P, Dekker R, Skarmeta-Gómez AF (2015) CEP-traj: an event-based solution to process trajectory data. Inf Syst 52:34–54

    Article  Google Scholar 

  40. Unit Nature & Biodiversity, DG Environment, European Commission: Natura 2000 data - the European network of protected sites (2016). https://www.eea.europa.eu/data-and-maps/data/natura-13. Accessed 26 Aug 2022

  41. Vespe M, Gibin M, Alessandrini A, Natale F, Mazzarella F, Osio GC (2016) Mapping EU fishing activities using ship tracking data. J Maps 12(sup1):520–525. https://doi.org/10.1080/17445647.2016.1195299

    Article  Google Scholar 

  42. Vouros GA, Doulkeridis C, Santipantakis G, Vlachou A, Pelekis N, Georgiou H, Theodoridis Y, Patroumpas K, Alevizos E, Artikis A, Fuchs G, Mock M, Andrienko G, Andrienko N, Ray C, Claramunt C, Camossi E, Jousselme AL, Scarlatti D, Cordero JM (2018) Big data analytics for time critical maritime and aerial mobility forecasting. In: EDBT. pp 612–623

  43. Wolfson O, Sistla A, Chamberlain S, Yesha Y (1999) Updating and querying databases that track mobile units. Distrib Parallel Dat 7(3):257–287

    Article  Google Scholar 

  44. Zhang D, Ding M, Yang D, Liu Y, Fan J, Shen HT (2018) Trajectory simplification: an experimental study and quality analysis. PVLDB 11(9):934–946

    Google Scholar 

Download references

Funding

This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070 and from the NSF grant number CCF-1563714. We would also like to thank MarineTraffic for providing the MS dataset..

Author information

Authors and Affiliations

  1. Department of Computer Science, Cornell University, Ithaca, USA

    Giannis Fikioris

  2. Information Management Systems Institute, Athena Research Center, Marousi, Greece

    Kostas Patroumpas

  3. Department of Maritime Studies, University of Piraeus, Piraeus, Greece

    Alexander Artikis

  4. Inst. of Informatics & Telecommunications, NCSR Demokritos, Athens, Greece

    Alexander Artikis & Georgios Paliouras

  5. Department of Computer Science, University of Liverpool, Liverpool, UK

    Manolis Pitsikalis

Authors
  1. Giannis Fikioris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kostas Patroumpas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Artikis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manolis Pitsikalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georgios Paliouras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giannis Fikioris.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fikioris, G., Patroumpas, K., Artikis, A. et al. Optimizing vessel trajectory compression for maritime situational awareness. Geoinformatica (2022). https://doi.org/10.1007/s10707-022-00475-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10707-022-00475-0

Keywords

  • AIS
  • Genetic algorithm
  • Maritime data analytics
  • Trajectory
  • Event recognition