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Forecasting the operational activities of the sea passenger terminal using intelligent technologies

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Modern transport systems are characterized by the development and implementation of intelligent transport technologies. Today, dynamic forecast models are not used in practice in the operation of a passenger terminal. Decision making is based on some regulatory values for passenger traffic, but this is not sufficient for efficient terminal management. Modern passenger terminals are characterized by dynamic proces variability and consideration of diverse options, taking into account the criteria of safety, reliability analysis, and the continuous research of passenger processing. For any modern marine passenger terminal, it is necessary to use the tool to simulate passenger flows in dynamics. Only in this way it is possible to obtain the analytical information and use it for decision making when solving the problem of the amount of personnel required for passenger service, transport safety, some forecasting tasks and so on. Of particular relevance is the choice of the mathematical transport model and the practical conditions for the implementation of the model in the real terminal operation. In this article, the analysis technique of intelligent simulation-based terminal services provides a new mathematical model of passenger movement inside the terminal and presents a new software instrument. Moreover, the conditions of implementation of some transportation models during the operation of marine passenger terminal are examined. The study represents an example of analytical information used for the forecast of the terminal operations, the analysis of the workload and the efficiency of the organization of the marine terminal.
Czasopismo
Rocznik
Strony
27--36
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
  • University of Dubrovnik, Croatia Electrical Engineering and Computing Department, Cira Carica 4, 20 000, Dubrovnik
autor
  • Saint-Petersburg State University of Aerospace Instrumentation 67, Bolshaya Morskaia, Saint-Petersburg, 190000, Russia
autor
  • Saint-Petersburg State University of Aerospace Instrumentation 67, Bolshaya Morskaia, Saint-Petersburg, 190000, Russia
Bibliografia
  • 1. Фетисов, В. & Майоров, Н. Практические задачи моделирования транспортных систем. Санкт-Петербург: ГУАП. 2012. 185 p. [In Russian: Fetisov, V. & Maiorov, N. Practical Problems of Modeling of Transport Systems. Saint-Petersburg: SUAI].
  • 2. Сольницев, Р.И. Модели и методы принятия проектных решений. Санкт-Петербург: ЛЭТИ. 2010. 68 p. [In Russian: Solnitsev, R.I. The models and methods of design solutions. Saint-Petersburg: LETI].
  • 3. Понятовский, В.В. Морские порты и транспорт (эволюция). Москва: РКонсульт. 2006. 429 p. [In Russian: Poniatovskii, V.V. Sea ports and transport (evolution). Moscow: RKonsult].
  • 4. Böse, J.W. Handbook of Terminal Planning. Springer Science Business Media, LLC. 2011. 456 p.
  • 5. Krile, S. Efficient Heuristic for Non-linear Transportation Problem on the Route with Multiple Ports. Polish Maritime Research. 2013. Vol. 20. No. 4. P. 80-86.
  • 6. Каталевский, Д.Ю. Основы имитационного моделирования и системного анализа в управлении. Москва: Дело. 2015. 496 p. [In Russian: Katalevskii, D.Yu. Basics of simulation modeling and systems analysis in management. Moscow: Delo].
  • 7. Helbing, D. & Molnár, P. & Farkas, I. & Bolay, K. Self-organizing pedestrian movement. Environment and Planning. 2001. Vol. 28. P. 361-383.
  • 8. Gipps, P.G. & Marksj¨o, B. A micro-simulation model for pedestrian flows. Mathematics and Computers in Simulation. 1985. Vol. 27. P.95-105.
  • 9. Akopov, A.S. & Beklaryan, L.A. Simulation of human crowd behavior in extreme situations. International Journal of Pure and Applied Mathematics. 2012. Vol. 79. No. 1. P. 121-138.
  • 10. Helbing, D. & Farkas, I. & Molnar, P. & Vicsek, T. Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and evacuation dynamics. 2002. No. 21. P. 21-58.
  • 11. Jugović, A. & Mezak, Vl. & Nikolić, G. Organization of Maritime Passenger Ports. Pomorski zbornik. 2006. Vol. 44 (1). P. 93-104.
  • 12. De Gooijer, J.G. & Hyndman, R.J. 25 years of time series forecasting. International Journal of Forecasting. 2006. Vol. 22(3). P. 443-473.
  • 13. Navinv, P.D. & Wheeler, R.J. Pedestrian flow characteristics. Traffic Engineering. 1969. Vol 39. P. 31-36.
  • 14. Løv°as, G.G. Modelling and simulation of pedestrian traffic flow. Transportation Research. 1994. No. B 28. P. 429-443.
  • 15. Helbing, D. & Molnar, P. Social force model for pedestrian dynamics. Physical Review.1995. Vol. E. 51. P. 4282-4286.
  • 16. Anylogic. Available at: http://www.anylogic.com/
  • 17. Software for Petri HPSim1.1. Available at: http://www.winpesim.de/
  • 18. Queueing Petri Nets (QPNs). Available at: http://ls4-www.cs.tu-dortmund.de/QPN/
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d288a26e-5a74-4170-87f4-211883a52cb7
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