skip to main content
10.1145/2486092.2486112acmconferencesArticle/Chapter ViewAbstractPublication PagespadsConference Proceedingsconference-collections
research-article

Modeling and simulation of crowd using cellular discrete event systems theory

Published:19 May 2013Publication History

ABSTRACT

In this paper, we discuss how Cellular Discrete Event System Specification (Cell-DEVS) theory can be used in modeling and simulation of the crowd. We will show that the efficient cell update mechanism of Cell-DEVS allows for more efficient entity-based simulation of the crowd compared to cellular automata. On the other hand the formal interfacing mechanisms provided by this theory allows for integration of other components such as DEVS atomic processing component or visualization and building information modeling components with the Cell-DEVS model. Finally, we describe in details of the design and development of several pedestrian models and present the results.

References

  1. Helbing D., Farkás I.J., Molnár P., Vicsek T. 2002 Simulation of pedestrian crowds in normal and evacuation situations, in: M. Schreckenberg, S.D. Sharma (Eds.), Pedestrian and Evacuation Dynamics, Springer, Berlin, (2002), 21--58.Google ScholarGoogle Scholar
  2. Blue V.J., Adler J.L. 2001 Cellular automata microsimulation for modeling bi-directional pedestrian walkways, Transportation Research Part B 35 (3) (2001) 293--312.Google ScholarGoogle Scholar
  3. Penn A., Turner A., Space syntax based agent simulation, in: M. Schreckenberg, S.D. Sharma (Eds.), Pedestrian and Evacuation Dynamics, Springer, Berlin, (2002), 99--114.Google ScholarGoogle Scholar
  4. Zhou, S., Chen, D., Cai, W., Luo, L., Low, M. Y. H., Tian, F., ... & Hamilton, B. D. (2010). Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation (TOMACS), 20(4), 20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Hughes, R. L. (2003). The flow of human crowds. Annual Review on Fluid Mechanics 35, 169--182.Google ScholarGoogle ScholarCross RefCross Ref
  6. Helbing, D., Farkas, I., and Vicsek, T. (2000). Simulating dynamical features of escape panic. Letters to Nature 407, 487--490.Google ScholarGoogle Scholar
  7. Castonguay, P., & Wainer, G. (2009, March). Aircraft evacuation DEVS implementation & visualization. In Proceedings of the 2009 Spring Simulation Multiconference. San Diego, CA. 144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000) Theory of modeling and simulation. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wainer, G. A. (2009). Discrete-event modeling and simulation: a practitioner's approach. CRC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Wolfram, S. (1986). Theory and applications of cellular automata.Google ScholarGoogle Scholar
  11. Hughes, R. L. (2003). The flow of human crowds. Annual Review on Fluid Mechanics 35, 169--182.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kisko, T. M., Francis, R. L., & Nobel, C. R. (1998). EVACNET4 User's Guide. University of Florida.Google ScholarGoogle Scholar
  13. Zhang, W. M., Huang, L., & Wang, B. (2009). Application of the EVACNET4 model to evacuation in high-rise building {J}. Fire Science and Technology, 3, 014.Google ScholarGoogle Scholar
  14. Chenney, S. (2004). Flow tiles. In Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation (pp. 233--242). Eurographics Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Deffuant, G. (2006). Comparing extremism propagation patterns in continuous opinion models. Journal of Artificial Societies and Social Simulation, 9(3).Google ScholarGoogle Scholar
  17. Salzarulo, L. (2006). A continuous opinion dynamics model based on the principle of meta-contrast. Journal of Artificial Societies and Social Simulation, 9(1).Google ScholarGoogle Scholar
  18. Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487--490.Google ScholarGoogle ScholarCross RefCross Ref
  19. Bandini, S., Manzoni, S., & Vizzari, G. (2006). Crowd Modeling and Simulation. Innovations in Design & Decision Support Systems in Architecture and Urban Planning, 105--120.Google ScholarGoogle Scholar
  20. Tao, W., & Jun, C. (2009). An Improved Cellular Automaton Model for Urban Walkway Bi-directional Pedestrian Flow. In Measuring Technology and Mechatronics Automation, 2009. ICMTMA'09. International Conference Vol. 3, pp. 458--461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Al-Zoubi, K. and Wainer, G. (2010). Distributed Simulation Using Restful Interoperability Simulation Environment (RISE) Middleware. Intelligence-Based Systems Engineering, Pages 129--157.Google ScholarGoogle Scholar
  22. Wang, S., Van Schyndel, M., Wainer, G., Rajus, V. S., & Woodbury, R. (2012). DEVS-based building information modeling and simulation for emergency evacuation. In Proceedings of the Winter Simulation Conference (p. 60). Winter Simulation Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Wright, F. L. (2009). Building Information Modeling.Google ScholarGoogle Scholar
  24. AutoDesk. (2013). "Autodesk Revit Architecture." Accessed Feb. 15. http://usa.autodesk.com/revit-architecture/.Google ScholarGoogle Scholar
  25. AutoDesk. (2013). "Autodesk 3ds Max." Accessed Feb. 15. http://usa.autodesk.com/3ds-max/.Google ScholarGoogle Scholar
  26. Freire, V., Wang, S., and Wainer, G. (2013). Visualization In 3ds Max For Cell-DEVS Models Based On Moving Entities. Symposium on Simulation for Architecture and Urban Design (SimAUD'13). San Diego, USA.Google ScholarGoogle Scholar

Index Terms

  1. Modeling and simulation of crowd using cellular discrete event systems theory

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        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

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 19 May 2013

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

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

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader