PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Operational model for vessel traffic using optimal control and calibration

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Due to the ever-increasing economic globalization, the scale of transportation through ports and waterways has increased sharply. As the capacity of maritime infrastructure in ports and inland waterways is limited, it is important to simulate vessel behavior to balance safety and capacity in restricted waterways. Currently many existing vessel simulation models focus mainly on vessel dynamics and maritime traffic in the open ocean. These models are, however, inapplicable to simulating vessel behavior in ports and inland waterways, because behavior in such areas can be influenced by many factors, such as waterway geometry, external conditions and human factors. To better simulate vessel behavior in ports and waterways, we developed a new maritime traffic model by adapting the theory of pedestrian models. This new model comprises two parts: the Route Choice Model and the Operational Model. The Route Choice Model has been demonstrated and calibrated in our recent study, in which the desired speed is generated. This paper presents the second part of the model, the Operational Model, which describes vessel behavior based on optimal control by using the output of the Route Choice Model. The calibration of the Operational Model is carried out as well. In the Operational Model, the main behavioral assumption is that all actions of the bridge team, such as accelerating and turning, are executed to force the vessel to sail with the desired speed and course. In the proposed theory, deviating from the desired speed and course, accelerating, decelerating and turning will provide disutility (cost) to the vessel. By predicting and minimizing this disutility, longitudinal acceleration and angular acceleration can be optimized. This way, the Operational Model can be used to predict the vessel speed and course. Automatic Identification System (AIS) data of unhindered vessel behavior in the Port of Rotterdam, the Netherlands, were used to calibrate the Operational Model. The calibration results produced plausible parameter values that minimized the objective function. The paths generated with these optimal parameters corresponded reasonably well to the actual paths.
Rocznik
Strony
70--77
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, the Netherlands Department of Transport & Planning
autor
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, the Netherlands Department of Transport & Planning
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, the Netherlands Department of Hydraulic Engineering
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, the Netherlands Department of Transport & Planning
Bibliografia
  • 1. AARSÆTHER, K.G. & MOAN, T. (2009) Estimating navigation patterns from AIS. Journal of Navigation. 62. pp. 587–607.
  • 2. DEGRE, T., GLANSDORP, C. & VAN DER TAK, C. (2003) The importance of a risk based index for vessels to enhance maritime safety. Proceedings of the the 10th IFAC Symposium on Control in Transportation Systems. Tokyo, Japan.
  • 3. FLEMING, W.H., SONER, H.M. & SONER, H.M. (2006) Controlled Markov processes and viscosity solutions. Springer.
  • 4. FOWLER, T.G. & SØRGÅRD, E. (2000) Modeling ship transportation risk. Risk Analysis. 20. pp. 225–244.
  • 5. HOOGENDOORN, S., DAAMEN, W., SHU, Y. & LIGTERINGEN, H. (2013) Modeling human behavior in vessel manoeuver simulation by optimal control and game theory. Transportation Research Record: Journal of the Transportation Research Board. 2326. pp. 45–53.
  • 6. HOOGENDOORN, S., HOOGENDOORN, R., WANG, M. & DAAMEN, W. (2012) Driver, driver support, and cooperative systems modeling by dynamic optimal control. Transportation Research Record: Journal of the Transportation Research Board. 2316. pp. 20–30.
  • 7. HSU, C.-I. & HSIEH, Y.-P. (2007) Routing, ship size, and sailing frequency decision-making for a maritime hub-andspoke container network. Mathematical and Computer Modelling. 45. pp. 899–916.
  • 8. KOSMAS, O. & VLACHOS, D. (2012) Simulated annealing for optimal ship routing. Computers & Operations Research. 39. pp. 576–581.
  • 9. MOU, J.M., TAK, C. V. D. & LIGTERINGEN, H. (2010) Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering. 37. pp. 483–490.
  • 10. NORSTAD, I., FAGERHOLT, K. & LAPORTE, G. (2011) Tramp ship routing and scheduling with speed optimization. Transportation Research Part C: Emerging Technologies. 19. pp. 853–865.
  • 11. PEDERSEN, P.T. (1995) Collision and grounding mechanics. Proceedings of the WEMT. Copenhagen.
  • 12. SARIÖZ, K. & NARLI, E. (2003) Assessment of manoeuvring performance of large tankers in restricted waterways: a real-time simulation approach. Ocean engineering. 30. pp. 1535–1551.
  • 13. SHU, Y., DAAMEN, W., LIGTERINGEN, H. & HOOGENDOORN, S. (2013) Vessel Speed, Course, and Path Analysis in the Botlek Area of the Port of Rotterdam, Netherlands. Transportation Research Board of the National Academies.
  • 14. SHU, Y., DAAMEN, W., LIGTERINGEN, H. & HOOGENDOORN, S. (2014) Vessel route choice model by optimal control and calibration. Proceedings of the IWNTM 2014: International Workshop on Nautical Traffic Models. Wuhan, China, 15– 17 October 2014.
  • 15. SHU, Y., DAAMEN, W., LIGTERINGEN, H. & HOOGENDOORN, S. (2015) Vessel route choice theory and modeling. Proceedings of the 94th Annual Meeting Transportation Research Board. Washington, USA, 11–15 January 2015; Authors version.
  • 16. SUTULO, S., MOREIRA, L. & GUEDES SOARES, C. (2002) Mathematical models for ship path prediction in manoeuvring simulation systems. Ocean engineering. 29. pp. 1–19.
  • 17. YOON, H.K. & RHEE, K.P. (2003) Identification of hydrodynamic coefficients in ship manoeuvering equations of motion by Estimation-Before-Modeling technique. Ocean Engineering. 30. pp. 2379–2404.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1bffa2b7-e3b4-4a4a-a5a8-7418389c010d
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.