Towards an Approach for Long Term AIS-Based Prediction of Vessel Arrival Times

  • Alexander Dobrkovic
  • Maria-Eugenia Iacob
  • Jos van Hillegersberg
  • Martin R. K. Mes
  • Maurice Glandrup
Chapter
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

The goal of this paper is to conduct a review of existing solutions and related algorithms on maritime route prediction using Automatic Information System (AIS) data, determine to what extent they can be applied to solve the prediction problem, and identify areas that have to be improved in order to get an industry-acceptable solution to enhance various logistics planning processes. The contributions of this paper are: (i) to present the available solutions for trajectory prediction of a vessel; (ii) to identify components that can be used for finding a solution for the identified problem as well as showing the strengths and weaknesses of each available option; and (iii) to propose a new concept for arrival time estimation based on trajectory prediction and the use of algorithms from the included literature review.

Keywords

Automatic Identification System Trajectory analysis Route prediction Maritime logistics Transportation planning 

Notes

Acknowledgment

This work is part of the R&D project Synchromodal-IT that is partly funded by the Dutch Institute of Advanced Logistics (DINALOG).

References

  1. Commission of the European Communities (2008). Document COM(2008) 310 final—2005/0239 COD, 11-06-2008Google Scholar
  2. Demšar U, Virrantaus K (2010) Space–time density of trajectories: exploring spatio-temporal patterns in movement data. Int J Geogr Inf Sci 24(10):1527–1542CrossRefGoogle Scholar
  3. Dutch Institute for Advanced Logistics (2013) SynchromodalIT. Retrieved 11 Aug 2014, from http://www.dinalog.nl/en/projects/r_d_projects/synchromodalit/
  4. Harati-Mokhtari A, Wall A, Brooks P, Wang J (2007) Automatic identification system (AIS): data reliability and human error implications. J Navig 60(03):373–389CrossRefGoogle Scholar
  5. Hornauer S, Hahn A (2013) Towards marine collision avoidance based on automatic route exchange. Paper presented at the control applications in marine systemsGoogle Scholar
  6. International Maritime Organization (2002) Safety of Life at Sea (SOLAS) convention Chapter V. Regulation 19Google Scholar
  7. Katsilieris F, Braca P, Coraluppi S (2013) Detection of malicious AIS position spoofing by exploiting radar information. Paper presented at the Information fusion (FUSION), 2013 16th international conference onGoogle Scholar
  8. Lampe OD, Kehrer J, Hauser H (2010) Visual analysis of multivariate movement data using interactive difference views. Paper presented at the VMVGoogle Scholar
  9. Laxhammar R, Falkman G, Sviestins E (2009) Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator. Paper presented at the information fusion, 2009. FUSION’09, 12th international conference onGoogle Scholar
  10. Lei P-R, Su J, Peng W-C, Han W-Y, Chang C-P (2011) A framework of moving behavior modeling in the maritime surveillance. J Chung Cheng Inst Technol 40(2):33–42Google Scholar
  11. Liu C, Chen X (2014) Vessel track recovery With incomplete AIS data using tensor CANDECOM/PARAFAC decomposition. J Navig 67(01):83–99CrossRefGoogle Scholar
  12. Ma SX, Sun J, Guan YQ (2013) Detection probability of airborne AIS. Appl Mech Mater 401:1204–1207CrossRefGoogle Scholar
  13. Oliveira LF, Gusovsky D (2012) Risk assessment of dropped and dragged anchors to offshore pipelines. Paper presented at the advances in safety, reliability and risk management—proceedings of the European safety and Reliability conference, ESREL 2011Google Scholar
  14. Pallotta G, Vespe M, Bryan K (2013) Vessel pattern knowledge discovery from ais data: a framework for anomaly detection and route prediction. Entropy 15(6):2218–2245CrossRefMATHGoogle Scholar
  15. Redoutey M, Scotti E, Jensen C, Ray C, Claramunt C (2008) Efficient vessel tracking with accuracy guarantees web and wireless geographical information systems. Springer, Berlin, pp 140–151Google Scholar
  16. Ristic B, La Scala B, Morelande M, Gordon N (2008) Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction. Paper presented at the information fusion, 2008 11th international conference onGoogle Scholar
  17. Talavera A, Aguasca R, Galván B, Cacereño A (2013) Application of Dempster-Shafer theory for the quantification and propagation of the uncertainty caused by the use of AIS data. Reliab Eng Syst Saf 111:95–105CrossRefGoogle Scholar
  18. Uiboupin R, Raudsepp U, Sipelgas L (2008) Detection of oil spills on SAR images, identification of polluters and forecast of the slicks trajectory. Paper presented at the US/EU-baltic international symposium, 2008 IEEE/OESGoogle Scholar
  19. Vespe M, Visentini I, Bryan K, Braca P (2012) Unsupervised learning of maritime traffic patterns for anomaly detection. Paper presented at the data fusion and target tracking conference (DF&TT 2012): algorithms and applications, 9th IETGoogle Scholar
  20. Wang Y, Zhang J, Chen X, Chu X, Yan X (2013) A spatial–temporal forensic analysis for inland–water ship collisions using AIS data. Saf Sci 57:187–202CrossRefGoogle Scholar
  21. Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. Manage Inf Syst Quart 26(2):3Google Scholar
  22. Wijaya WM, Nakamura Y (2013) Predicting ship behavior navigating through heavily trafficked fairways by analyzing AIS data on apache HBase. Paper presented at the computing and networking (CANDAR), 2013 first international symposium onGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexander Dobrkovic
    • 1
  • Maria-Eugenia Iacob
    • 1
  • Jos van Hillegersberg
    • 1
  • Martin R. K. Mes
    • 1
  • Maurice Glandrup
    • 1
  1. 1.Faculty of Behavioural, Management and Social SciencesUniversity of TwenteEnschedeThe Netherlands

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