Agent-based evacuation in passenger ships using a goal-driven decision-making model

• Autorzy: Ni B. , Li Z. , Li X.
• Języki publikacji: EN

Abstrakty

EN

A new agent-based model is proposed to support designers in assessing the evacuation capabilities of passenger ships and in improving ship safety. It comprises models for goal-driven decision-making, path planning, and movement. The goal-driven decision-making model determines an agent’s target by decomposing abstract goals into subgoals. The path-planning model plans the shortest path from the agent’s current position to its target. The movement model is a combination of social-force and steering models to control the agent in moving along its path. The utility of the proposed model is verified using 11 tests for passenger ships proposed by the Maritime Safety Committee of the International Maritime Organization

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: 24
• Numer: 2
• Opis fizyczny: p.56-67,fig.,ref.

Twórcy

autor

Ni B.

• College of Ship Building, Harbin Engineering University, 15001 Harbin, China

autor

Li Z.

• College of Mechanical and Electrical Engineering, Harbin Engineering University, No.145 Nantong Street, Nangang District, 150001 Harbin, China

autor

Li X.

• Dalian Neusoft University of Information, No.8 Software Park Road, 116023 Dalian, China

Bibliografia

• 1. IMO: Guidelines for evacuation analysis for new and existing passenger ships. International Maritime Organi-zation, pp. 1–46, 2007.
• 2. Wolfram, S.: Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), pp. 601–644, 1983.
• 3. Burstedde, C., Klauck, K., Schadschneider, A. and Zit-tartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A: Statistical Mechanics and its Applications, 295(3–4), pp. 507–525, 2001.
• 4. Guo, R. Y.: New insights into discretization effects in cel-lular automata models for pedestrian evacuation. Physica A: Statistical Mechanics and its Applications, 400, pp. 1–11, 2014.
• 5. Guo, R. Y., Huang, H. J. and Wong, S. C.: Route choice in pedestrian evacuation under conditions of good and zero visibility: Experimental and simulation results. Transportation Research Part B: Methodological, 46(6), pp. 669–686, 2012.
• 6. Guo, R.-Y., Huang, H.-J. and Wong, S. C.: A potential field approach to the modeling of route choice in pedestrian evacuation. Journal of Statistical Mechanics: Theory and Experiment, 2013(2), p. P02010, 2013.
• 7. Tang, T. Q., Chen, L., Guo, R. Y. and Shang, H. Y.: An evacuation model accounting for elementary students’ individual properties. Physica A: Statistical Mechanics and its Applications, 440, pp. 49–56, 2015.
• 8. Ha, S., Ku, N. K., Roh, M. Il and Lee, K. Y.: Cell-based evacuation simulation considering human behavior in a passenger ship. Ocean Engineering, 53, pp. 138–152, 2012.
• 9. Park, K. P., Ham, S. H. and Ha, S.: Validation of advanced evacuation analysis on passenger ships using experimen-tal scenario and data of full-scale evacuation. Computers in Industry, 71, pp. 103–115, 2015.
• 10. Helbing, D. and Molnár, P.: Social force model for pedes-trian dynamics. Physical Review E, 51(5), pp. 4282–4286, 1995.
• 11. Helbing, D., Farkas, I. and Vicsek, T.: Simulating dynamical features of escape panic. Nature, 407(6803), pp. 487–490, 2000.
• 12. Yuen, J. K. K. and Lee, E. W. M.: The effect of overtaking behavior on unidirectional pedestrian flow. Safety Sci-ence, 50(8), pp. 1704–1714, 2012.
• 13. Heliövaara, S., Korhonen, T., Hostikka, S. and Ehtamo, H.: Counterflow model for agent-based simulation of crowd dynamics. Building and Environment, 48(1), pp. 89–100, 2012.
• 14. Xu, M., Wu, Y., Lv, P., Jiang, H., Luo, M. and Ye, Y.: MiSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation. Neurocomputing, 168, pp. 529–537, 2015.
• 15. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl. 3), pp. 7280–7287, 2002.
• 16. Tan, L., Hu, M. and Lin, H.: Agent-based simulation of building evacuation: Combining human behavior with predictable spatial accessibility in a fire emergency. Information Sciences, 295, pp. 53–66, 2015.
• 17. Tang, F. and Ren, A.: GIS-based 3D evacuation simulation for indoor fire. Building and Environment, 49(1), pp. 193–202, 2012.
• 18. Hart, P. E., Nilsson, N. J. and Raphael, B.: Correction to ‘A Formal Basis for the Heuristic Determination of Minimum Cost Paths’. ACM SIGART Bulletin, 37(37), pp. 28–29, 1972.
• 19. Thompson, P. S. and Marchant, E. W.: Testing and Appli-cation of the Computer Model ‘SIMULEX’. Fire Safety Journal, 24(2), pp. 149–166, 1995.
• 20. Korhonen, T. and Hostikka, S.: Fire Dynamics Simula-tor with Evacuation: FDS+Evac, Technical Reference and User’s Guide (FDS 5.5.0, Evac 2.2.1). VTT Technical Research Centre of Finland, 2010.
• 21. Reynolds, C. W.: Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), pp. 25–34, 1987.
• 22 . Ha r t ma n, C. a nd Beneš, B.: Autonomous boids. 17(3– 4), pp. 199–206, 2006.
• 23. Langston, P. A., Masling, R. and Asmar, B. N.: Crowd dynamics discrete element multi-circle model. Safety Science, 44(5), pp. 395–417, 2006.
• 24. Russell, S. J. and Norvig, P.: Artificial Intelligence: A Modern Approach. Third Edit, Prentice Hall, Upper Saddle River, 2010.
• 25. Thunderhead Engineering: Pathfinder:Verification and Validation. Pathfinder, Thunderhead Engineering, Man-hattan, 2016.

Identyfikatory

DOI

10.1515/pomr-2017-0050