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Modelling of Risk and Reliability of Maritime Transport Services

  • Milena StróżynaEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

Maritime transport plays nowadays an important role in the global economy. In 2017 around 80% of trade was carried by sea, therefore there is a need for constant monitoring of transport processes from the point of view of their reliability and punctuality. In order to provide up-to-date information about reliability and punctuality, a lot of data from different maritime sources needs to be collected and analysed. The paper presents concepts of two methods that based on an analysis of big amount of maritime data provide information that might be used to support various entities from the maritime domain in decision-making. The first method concerns a short-term assessment of reliability of a maritime transport service, while the second one dynamically predicts punctuality of a ship. The presented methods are part of a PhD research. The aim of the article is to provide an overview of this research, starting from motivation, its objectives and the thesis, through presentation of the methods, up to description of the main results of methods’ evaluation.

References

  1. 1.
    UNCTAD: Review of maritime transport 2017 (2017)Google Scholar
  2. 2.
    Vernimmen, B., Dullaert, W., Engelen, S.: Schedule unreliability in liner shipping: origins and consequences for the hinterland supply chain. Marit. Econ. Logist. 9(3), 193–213 (2007)CrossRefGoogle Scholar
  3. 3.
    Calkoen, C., Santbergen, P.: MetOcean services to the marine transport sector. Deliverable of Melodies Project (2016)Google Scholar
  4. 4.
    Veldhuis, H.D.: Developing an automated solution for ETA definition concerning long distance shipping. Ph.D. thesis, University of Twente (2015)Google Scholar
  5. 5.
    Goerlandt, F., Montewka, J.: Maritime transportation risk analysis: review and analysis in light of some foundational issues. Reliab. Eng. Syst. Saf. 138, 115–134 (2015)CrossRefGoogle Scholar
  6. 6.
    ABS: Guidance notes on risk assessement applications for the marine and offshore oil and gas industries. Technical report, American Bureau of Shipping (2000)Google Scholar
  7. 7.
    Szymanek, A.: Risk acceptation principles in transport. J. KONBiN 5(2), 271–281 (2008)CrossRefGoogle Scholar
  8. 8.
    Soares, C.G., Teixeira, A.P.: Risk assessment in maritime transportation. Reliab. Eng. Syst. Saf. 74(3), 299–309 (2001)CrossRefGoogle Scholar
  9. 9.
    Berle, Ø., Asbjørnslett, B.E., Rice, J.B.: Formal vulnerability assessment of a maritime transportation system. Reliab. Eng. Syst. Saf. 96(6), 696–705 (2011)CrossRefGoogle Scholar
  10. 10.
    Trucco, P., Cagno, E., Ruggeri, F., Grande, O.: A Bayesian belief network modelling of organisational factors in risk analysis: a case study in maritime transportation. Reliab. Eng. Syst. Saf. 93(6), 823–834 (2008)CrossRefGoogle Scholar
  11. 11.
    Balmat, J.F., Lafont, F., Maifret, R., Pessel, N.: MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor. Ocean Eng. 36(15–16), 1278–1286 (2009)CrossRefGoogle Scholar
  12. 12.
    Elsayed, T.: Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals. Appl. Ocean Res. 31(3), 179–185 (2009)CrossRefGoogle Scholar
  13. 13.
    Kowalski, J., Kozera, J.: Mapa zagrożeń bezpieczeństwa energetycznego RP w sektorach ropy naftowej i gazu ziemnego. In: Bezpieczeństwo Narodowe, pp. 301–324. BBN, Warszawa (2009)Google Scholar
  14. 14.
    Blaich, M., Köhler, S., Reuter, J., Hahn, A.: Probabilistic collision avoidance for vessels. IFAC-PapersOnLine 48(16), 69–74 (2015)CrossRefGoogle Scholar
  15. 15.
    Hornauer, S., Hahn, A.: Towards marine collision avoidance based on automatic route exchange. IFAC Proc. Vol. 46(33), 103–107 (2013)CrossRefGoogle Scholar
  16. 16.
    Wieteska, G.: Zarządzanie ryzykiem w łacuchu dostaw na rynku B2B. Difin (2011)Google Scholar
  17. 17.
    Hevner, A., March, S., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  18. 18.
    Hevner, A., Chatterjee, S.: Design Science Research in Information Systems. Springer, Boston (2010).  https://doi.org/10.1007/978-1-4419-5653-8CrossRefGoogle Scholar
  19. 19.
    Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25(1), 77–89 (2016)CrossRefGoogle Scholar
  20. 20.
    Norkus, O., Sauer, J.: RABIC: a reference architecture for business intelligence in the cloud. J. Commun. Comput. 13, 244–260 (2016)Google Scholar
  21. 21.
    MMO: Mapping UK Shipping Density and Routes from AIS. Technical report, Marine Managment Organisation, Newcastle, UK (2014). MMO Project No: 1066Google Scholar
  22. 22.
    Shelmerdine, R.L.: Teasing out the detail: how our understanding of marine AIS data can better inform industries, developments, and planning. Mar. Policy 54, 17–25 (2015)CrossRefGoogle Scholar
  23. 23.
    Wu, L., Xu, Y., Wang, Q., Wang, F., Xu, Z.: Mapping global shipping density from AIS data. J. Navig. 70(1), 67–81 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Poznań University of Economics and BusinessPoznańPoland

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