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A marine traffic simulation system for hub ports

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Published:19 May 2013Publication History

ABSTRACT

Ensuring congestion-free marine traffic is crucial for hub ports in the world. For these hub ports, there has been increasing demand in marine transport. Therefore a capacity-assessment tool that models and simulates the navigational network, the traffic flows and complex navigational behaviors of vessels is needed. The simulation model presented in this paper is unique in the scale and complexity of the waterway networks covered, the flexibility in defining the traffic flow patterns, and the degree of accuracy demanded of navigational behaviors. As such, none of the existing models and simulation tools is adequate for assessing the waterway capacity. The model was calibrated based on detailed analysis of historical records and consultations with domain experts. The model was used in the study of several future scenarios of a hob port. The simulation system built has laid a useful foundation for planning future marine traffic for hub ports.

References

  1. International Maritime Organization (1997): Interim Guideline for the Application of Formal Safety Assessment for the IMO Rule-Making Process, MSC/Circ.829 & MEPC/Circ.335.Google ScholarGoogle Scholar
  2. CDR S.M. Neill (2001): A Survey of Waterway Capacity and Policy Issues. Web Access: http://gulliver.trb.org/conferences/2001Waterway&Harbor/Neill.pdfGoogle ScholarGoogle Scholar
  3. Permanent International Association of Navigation Congresses (1995): Approach Channels: A Guide for Design.Google ScholarGoogle Scholar
  4. International Association of Lighthouse Authorities (2000): IALA Guidelines on Risk Management.Google ScholarGoogle Scholar
  5. T. Huet, T. Osman, and C. Ray. Modeling traffic navigation network with a multi-agent platform. In Proceedings of 17th European Simulation Multi-conference - SCS Europe, pages 111--117, Nottingham, UK, June 2003.Google ScholarGoogle Scholar
  6. A. N. Ince and E. Topuz. Modeling and simulation for safe and efficient navigation in narrow waterways. Journal of navigation, 57(1), 53--71, 2004.Google ScholarGoogle Scholar
  7. R. D. Colwill, D. Wignall, and I. Dand. The applications of marine risk simulation to the nearcasting and prevention of collision incidents. In VTS 2004 - the 10th Int'l Symposium on Vessel Traffic Services, 2004.Google ScholarGoogle Scholar
  8. H. Fan and J.-M. Cao. Sea space capacity and operation strategy analysis system. Transportation Planning and Technology, 24(1), 49--63, January 2000.Google ScholarGoogle Scholar
  9. A. Frima. Capacity study for the rio de la plata waterway, Argentina. Master's thesis, Delft University of Technology, October 2004.Google ScholarGoogle Scholar
  10. J. Golkar, A. Shekhar, and S. Buddhavarapu. Panama canal simulation model. In WSC '98: Proceedings of the 30th conference on Winter Simulation, pages 1229--1238. IEEE Computer Society Press, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. K. Hasegawa, G. Tashiro, S. Kiritani, and K. Tachikawa. Intelligent marine traffic simulator for congested waterways. In Proceedings of International Symposium for Young Researchers on Modeling and their Applications, pages 181--186, October 2002.Google ScholarGoogle Scholar
  12. Shell Ying Huang, Wen Jing Hsu and Yuxiong He. (2011). Assessing capacity and improving utilization of anchorages. Transportation Research Part E: Logistics and Transportation Review, 47(2), 216--227.Google ScholarGoogle ScholarCross RefCross Ref
  13. E. Kose, E. Basar, E. Demirci, A. Guneroglu, and S. Erkebay. Simulation of marine traffic in Istanbul strait. Simulation Modeling Practice and Theory, 11, 597--608, October 2003.Google ScholarGoogle Scholar
  14. J. R. van Dorp, J. R. W. Merrick, J. R. Harrald,T. A. Mazzuchi, and M. Grabowski. (2001). A risk management procedure for the Washington state ferries. Risk Analysis, 21(1), 127--142.Google ScholarGoogle ScholarCross RefCross Ref
  15. K. Hasegawa, Some Recent Developments of Next Generation's Marine Traffic Systems, CAMS 2004.Google ScholarGoogle Scholar
  16. L.A.G. Franzese, L.O. Abdenur, R.C. Botter, D. Starks, and A.R. Cano. Simulating the Panama Canal: Present and Future. In Proc. of the 2004 Winter Simulation Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. B. Park and J.D. Schneeberger, Microscopic Simulation Model Calibration and Validation: Case Study of VISSIM Simulation Model for a Coordinated Actuated Signal System, Transportation Research Record (TRB) 2003 Annual Meeting.Google ScholarGoogle Scholar
  18. E. Brockfeld, R.D. Kuhne, and P. Wagner, Calibration and Validation of Microscopic Traffic Flow MOdels, Transportation Research Record (TRB) 2004 Annual Meeting.Google ScholarGoogle Scholar
  19. L. Chu, H.X. Liu, J. Oh, and W. Recker. A Calibration Procedure for Microscopic Traffic Simulation. Transportation Research Record (TRB) 2004 Annual Meeting.Google ScholarGoogle Scholar
  20. K. Hasegawa. 1993. Knowledge-based Automatic Navigation System for Harbour Maneuvering. Proc. Of 10th Ship Control Systems Symposium, pp.67--90.Google ScholarGoogle Scholar
  21. CheeKuang Tam, Richard Bucknall and Alistair Greig. 2009. Review of Collision Avoidance and Path Planning Methods for Ships in Close Range Encounters. The Journal of Navigation, 62, 455--476.Google ScholarGoogle ScholarCross RefCross Ref
  22. Davis, P. V., M. J. Dove & C. T. Stockel. 1980. A computer simulation of marine traffic using domains and arenas, The Journal of Navigation, 33, 215--222.Google ScholarGoogle ScholarCross RefCross Ref
  23. UNCTAD. 2010. Merchant fleet by flag of registration and by type of ship, annual, 1980--2010. http://unctadstat.unctad.org/TableViewer/tableView.aspx?ReportId=93Google ScholarGoogle Scholar

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      cover image ACM Conferences
      SIGSIM PADS '13: Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
      May 2013
      426 pages
      ISBN:9781450319201
      DOI:10.1145/2486092

      Copyright © 2013 ACM

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      Publication History

      • Published: 19 May 2013

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      SIGSIM PADS '13 Paper Acceptance Rate29of75submissions,39%Overall Acceptance Rate398of779submissions,51%

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