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Safe shipping is essential for society and different measures are taken to improve maritime safety, for example through implementation of traffic separation schemes, radar surveillance and traffic management concepts. But how can maritime safety be measured to determine the effects of those implementations? In this study, a real-time maritime safety index for a ship is developed, taking into account both the probability of grounding and the probability of collision. The index is developed using fuzzy integrated systems and validated in ship handling simulator scenarios. It uses numerical data from the simulator as an input to assess the present traffic situation from the perspective of a specific ship and outputs a comprehensive index. This paper describes the concept of sea traffic management as proposed and evaluated in the EU funded STM Validation project, the motivation for developing a maritime safety index, the numerical input variables and model properties and also validates the feasibility of the approach.
Rocznik
Tom
Strony
469--475
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
- Chalmers University of Technology, Gothenburg, Sweden
autor
- Fraunhofer Center for Maritime Logistics and Services CML, Hamburg, Germany
autor
- Fraunhofer Center for Maritime Logistics and Services CML, Hamburg, Germany
autor
- SSPA Sweden AB, Gothenburg, Sweden
Bibliografia
- 1 Bai, W. W. (2006). Fundamentals of Fuzzy Logic Control ‐ Fuzzy Sets, Fuzzy Rules and Defuzzifications. Advanced Fuzzy Logic Technologies in Industrial Applications. Springer ‐ Verlag.
- 2 International Maritime Organization. (2003). Convention on the International Regulations for Preventing Collisions at Sea. London.
- 3 Kao, S. L., Lee, K., Chang, K. Y., & Ko, M.‐D. (2007). A Fuzzy Logic Method for Collision Avoidance in Vessel Traffic Service. Journal of Navigation, S. 17‐31.
- 4 Kozlowska, E. (2012). Basic principles of fuzzy logic. Von Prague: Czech Technical University in Prague: http://access.feld.cvut.cz/view.php?cisloclanku=2012080 002 abgerufen
- 5 Lind, M., Hägg, M., Siwe, U., & Haraldson, S. (2016). Sea Traffic Management – Beneficial for all Maritime Stakeholders. Proceedings of 6th Transport Research Arena, (S. 183‐192). Warsaw.
- 6 Lopez‐Santander, A., & Lawry, J. (2017). An Ordinal Model of Risk Based on Marinerʹs Judgement. The Journal of Navigation, 70, S. 309‐324.
- 7. Mamdani, E. A. (1975). An Experiment in Linguistic Synthesis With a Fuzzy Logic Controller. International Journal of Man‐Machine Studies, 7, S. 1‐13.
- 8 Olindersson, F., & Janson, C.‐E. (2015). Development of a Software to Identify and Analyse Marine Traffic Situations. MARSIM. Newcastle, UK
- 9 Park, G.‐K. K.‐Y. (2012). Building an Intelligent Supporting System for Safe Navigation Using Fuzzy Theory. Proceedings of 2012 International Conference on Fuzzy Theory and Its Applications. Nagional Chung Hsing University, Taiwan.
- 10 Perera, L. C. (2011). Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Journal of Marine Science and Technology, S. 84‐99.
- 11 Rizvanolli, A., Burmeister, H.‐C., & John, O. (2015). The role of the European Maritime Simulator Network in assessing dynamic sae traffic management principles. TransNav (Vol. 9, Nr.4) (S. 559‐564). Gdynia: Gdynia Maritime University, Faculty of Navigation
- 12 Sjöfartsverket. (2016). Sea Traffic Management Validation Project. Retrieved November 01, 2016, from http://stmvalidation.eu/
- 13 Zadeh, L. (1965). Fuzzy Sets. In Information and Control (S. 338‐353).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-d0d01124-d921-48dd-a21d-a717c0683e85