Skip to main content
SpringerLink
Log in

Real-time vessel behavior prediction

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Vessel traffic management systems (VTMS) and vessel traffic monitoring information systems (VTMIS) have been available for a number of years now. These systems have significantly contributed to increasing the efficiency and safety of operations at sea. However, nowadays, risks at sea are once again on the rise, thus demanding an evolution in VTMS and VTMIS, such that they can support a human operator’s better understanding of the complex reality at sea and enhance his or her decision-making in light of danger. A critical requirement of such systems, is that they exhibit the ability to for-see unfolding cautious and potentially hazardous situations, so as to propose measures of danger avoidance. In this study, we employ machine learning, and specifically artificial neural networks, as a tool to add predictive capacity to VTMIS. The main objective of this study is to implement a publicly accessible, web-based system capable of real time learning and accurately predicting any vessels future behavior in low computational time. This work describes our approach, design choices, implementation and evaluation details, while we present a proof of concept prototype system. Our proposal can potentially be used as the predictive foundation for various intelligent systems, including vessel collision prevention, vessel route planning, operation efficiency estimation and even anomaly detection systems.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • ANAVE (2013) Merchant marine and maritime transport 2012/2013

  • Azoff EM (1994) Neural network time series forecasting of financial markets

  • Bevilacqua V (2006) Hidden markov models for recognition using artificial neural networks. International conference of intellignet computing. Springer, Berlin, p 1331

    Google Scholar 

  • Bomberger N, Rhodes B, Seibert M, Waxman A (2006) Associative learning of vessel motion patterns for maritime situation awareness. 2006 9th international conference information fusion. IEEE, pp 1–8

  • Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis, forecasting and control, 3rd edn. Englewood Cliffs, Prentice Hall

  • Braga AP, Ludermir TB, Carvalho ACPLF (2000) Artificial neural networks. LTC, Rio de Janeiro

  • Cai Q, He H, Man H (2013) Spatial outlier detection based on iterative self-organizing learning model. Neurocomputing 117:161–172. doi:10.1016/j.neucom.2013.02.007

    Article  Google Scholar 

  • Chadwick J, Snyder T, Panda H (2012) Programming ASP.NET MVC 4: developing Real-world web applications with ASP.NET MVC. p 492

  • Ebada AMAM (2005) Prediction of ship turning manoeuvre using artificial neural networks. 8th Numer. Towing Tank Symp

  • Endsley MR (1988) Design and evaluation for situation awareness enhancement. Proc Hum Factors Ergon Soc Annu Meet 32:97–101. doi:10.1177/154193128803200221

    Google Scholar 

  • Galloway J, Haack P, Wilson B, Allen KS (2012) Professional ASP.NET MVC 4 (Wrox Professional Guides). p 504

  • Gomes GSS, Ludermir TB (2013) Optimization of the weights and asymmetric activation function family of neural network for time series forecasting. Expert Syst Appl 40:6438–6446

    Article  Google Scholar 

  • Gomes L, Faria P, Morais H et al (2014) Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intell Syst 29:56–65. doi:10.1109/MIS.2013.2

    Google Scholar 

  • Heaton J (2008) Introduction to neural networks for C#, 2nd Edn. Heaton Research, p 428. http://www.amazon.com/Introduction-Neural-Networks-2nd-Edition/dp/1604390093

  • Heaton J (2011) Programming neural networks with Encog3 in C#, 2nd Edn. Heaton Research, p 240. http://www.amazon.com/Programming-Neural-Networks-Encog3-2nd/dp/1604390263

  • Karlaftis MG, Vlahogianni EI (2011) Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp Res Part C Emerg Technol 19:387–399

    Article  Google Scholar 

  • Karlik B, Olgac AV (2010) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1:111

    Google Scholar 

  • Krishnakumar K (2003) Intelligent systems for aerospace engineering—an overview. National Aeronautics & Space Administration, Moffet field Ca Ames Research Center

  • Lagerweij R, Vries G de, Someren M van (2009) Learning a model of ship movements. Thesis for Bachelor of Science-Artificial Intelligence, University of Amsterdam

  • Laxhammar R, Falkman G, Sviestins E (2015) Anomaly detection in sea traffic–a comparison of the gaussian mixture model and the kernel density estimator. In: 12th international conference on information fusion, 2009. FUSION '09. IEEE, Seattle, WA, pp 756–763

  • Lopez A, Perez R, Moreno B (2011) Forecasting Performance and M-Competition. Does the accuracy measure matter? Int. Stat. Inst. Proc. 58th World Stat. Congr

  • Makridakis S, Hibon M (2000) The M3-competition: results, conclusions and implications. Int J Forecast 16:451–476. doi:10.1016/S0169-2070(00)00057-1

    Article  Google Scholar 

  • Makridakis S, Andersen A, Carbone R et al (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1:111–153. doi:10.1002/for.3980010202

    Article  Google Scholar 

  • Makridakis S, Chatfield C, Hibon M et al (1993) The M2-competition: a real-time judgmentally based forecasting study. Int J Forecast 9:5–22. doi:10.1016/0169-2070(93)90044-N

    Article  Google Scholar 

  • Mostafa MM (2004) Forecasting the Suez Canal traffic: a neural network analysis. Marit Policy Manag 31:139–156. doi:10.1080/0308883032000174463

    Article  Google Scholar 

  • Nicolau V, Aiordachioaie D, Popa R (2004) Neural network prediction of the wave influence on the yaw motion of a ship. 2004 IEEE international joint conference neural networks (IEEE Cat. No. 04CH37541). IEEE, pp 2801–2806

  • Pankratz A (1983) Forecasting with univariate box-jenkins models: concepts and cases. Wiley, New York

    Book  Google Scholar 

  • Perera LP, Oliveira P, Guedes Soares C (2012) Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Trans Intell Transp Syst 13:1188–1200. doi:10.1109/TITS.2012.2187282

    Article  Google Scholar 

  • Rhodes BJ, Bomberger NA, Zandipour M (2007) Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness. 2007 10th International conference information fusion. IEEE, pp 1–8

  • Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE international conference neural networks. IEEE, pp 586–591

  • Rothblum AM (2002) Human error and marine safety. 2ND Int. Work. Hum. FACTORS OFFSHORE Oper

  • Santos B, Lunday K (2009) Maritime domain awareness. Coast Guard J Saf Secur Sea, In: Proceedings of marine safety and security council, p 66

  • Simsir U, Ertugrul S (2009) Prediction of manually controlled vessels’ position and course navigating in narrow waterways using artificial neural networks. Appl Soft Comput 9:1217–1224

    Article  Google Scholar 

  • Souza BA, Brito NSD, Neves WLA, et al (2004) Comparison between backpropagation and RPROP algorithms applied to fault classification in transmission lines. 2004 IEEE international joint conference neural networks (IEEE Cat. No.04CH37541). IEEE, pp 2913–2918

  • Stopford M (2009) Maritime Economics, 3rd edn. Routledge, p 840

  • US Department of Homeland Security (2005) National plan to achieve maritime domain awareness (MDA)

  • Verber D (2012) Implementation of massive artificial neural networks with CUDA. Cut Edge Res New Technol. doi:10.5772/2431

    Google Scholar 

  • Westrenen F, Praetorius G (2012) Maritime traffic management: a need for central coordination? Cogn Technol Work. doi:10.1007/s10111-012-0244-5

    Google Scholar 

  • Zandipour M, Rhodes B, Bomberger N (2008) Probabilistic prediction of vessel motion at multiple spatial scales for maritime situation awareness. 11th international conference information fusion

  • Zhang G, Eddy Patuwo B, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by MarineTraffic Research.

Author information

Authors and Affiliations

  1. Department of Product and Systems Design Engineering, University of the Aegean, Syros, Greece

    Dimitrios Zissis, Elias K. Xidias & Dimitrios Lekkas

Authors
  1. Dimitrios Zissis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elias K. Xidias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dimitrios Lekkas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitrios Zissis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zissis, D., Xidias, E.K. & Lekkas, D. Real-time vessel behavior prediction. Evolving Systems 7, 29–40 (2016). https://doi.org/10.1007/s12530-015-9133-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-015-9133-5

Keywords

Search

Navigation