An Indoor Crowd Simulation Using a 2D-3D Hybrid Data Model

  • Chulmin Jun
  • Hyeyoung Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5592)

Abstract

Recent LBS-related technologies tend to extend to indoor spaces using localization sensors such as RFID. In order to implement real time evacuation applications, at least two problems must be resolved in advance; first, proper indoor data models and implementation methods that can accommodate evacuees positioning and routing computations should be available, second, evacuation simulations also need to be performed using the same indoor databases for consistent integration. However, none of these have been suggested explicitly as of now. Although some 3D modeling studies have dealt with topological structures, they are mainly focused on outer building volumes and it is difficult to incorporate such theoretical topology into indoor spaces due to complexity and computational limitations. In this study, we suggest an alternative method to build a 3D indoor model with less cost. It is a 2D-3D hybrid data model that combines the 2D topology constructed from CAD floor plans and the 3D visualization functionality. We show the process to build the proposed model in a spatial DBMS and visualize in 2D and 3D. Also, we illustrate a test CA(cellular automata)-based 3D crowd simulation using our model.

Keywords

Crowd simulation spatial DBMS 2D-3D hybrid data model CA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arens, C.A.: Maintaining reality: modelling 3D spatial objects in a GeoDBMS using a 3D primitive. M.Sc. Thesis, Delft University of Technology, The Netherlands (2003)Google Scholar
  2. 2.
    Blue, V.J., Adler, J.L.: Using cellular automata microsimulation to model pedestrian movements. In: Ceder, A. (ed.) Proceedings of the 14th International Symposium on Transportation and Traffic Theory, Jerusalem, Israel, pp. 235–254 (1999)Google Scholar
  3. 3.
    Boston Geographic Information Systems, http://www.bostongis.com
  4. 4.
    Chen, T.K., Abdul-Rahmana, A., Zlatanova, S.: 3D Spatial Operations for geo-DBMS: geometry vs. topology. In: International Archives of XXIth Congress of the ISPRS 2008, Part B2, Beijing (2008)Google Scholar
  5. 5.
    Ellul, C., Haklay, M.: Using a B-rep structure to query 9-intersection topological relationships in 3D GIS – reviewing the approach and improving performance. In: Lee, J., Zlatanova, S. (eds.) 3D Geo-information Sciences, pp. 15–31. Springer, Berlin (2008)Google Scholar
  6. 6.
    Gröger, G., Reuter, M., Plümer, L.: Representation of a 3-D city model in spatial object-relational databases. In: Proc. of the 20th Congress of International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey (2004)Google Scholar
  7. 7.
    Hamacher, H.W., Tjandra, S.A.: Mathematical modelling of evacuation problems- a state of art. In: Schreckenberg, M., Sharma, S. (eds.) Pedestrian and Evacuation Dynamics, pp. 227–266. Springer, Berlin (2001)Google Scholar
  8. 8.
    Helbing, D., Molnár, P.: Self-organization phenomena in pedestrian crowds. In: Schweitzer, F. (ed.) Self-Organisation of Complex Structures: From Individual to Collective Dynamics, Gordon & Beach, London (1997)Google Scholar
  9. 9.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  10. 10.
    Helbing, D., Farkas, I., Molnár, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg, M., Sharma, S. (eds.) Pedestrian and Evacuation Dynamics, pp. 21–58. Springer, Berlin (2001)Google Scholar
  11. 11.
    Henein, C.M., White, T.: Agent-based modelling of forces in crowds. In: Davidsson, P., Logan, B., Takadama, K. (eds.) MABS 2004. LNCS, vol. 3415, pp. 173–184. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Henein, C., White, T.: Macroscopic effects of microscopic forces between agents in crowd models. Physica A 373, 694–712 (2007)CrossRefGoogle Scholar
  13. 13.
    Hillier, B.: Space is the Machine. Cambridge University Press, Cambridge (1996)Google Scholar
  14. 14.
    Hillier, B., Hanson, J.: The Social Logic of Space. Cambridge University Press, Cambridge (1984)CrossRefGoogle Scholar
  15. 15.
    Jiang, B., Claramunt, C., Batty, M.: Geometric accessibility and geographic information: extending desktop GIS to space syntax, Computers. Environment and Urban Systems 23, 127–146 (1999)CrossRefGoogle Scholar
  16. 16.
    Kim, H., Jun, C.: Indoor spatial analysis using Space syntax. In: International Archives of XXIth Congress of the ISPRS 2008, Beijing, WG II/1, pp. 1065–1070 (2008)Google Scholar
  17. 17.
    Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionicsinspired cellular automaton model for pedestrian dynamics. Physica A 312, 260–276 (2002)CrossRefMATHGoogle Scholar
  18. 18.
    Klupfel, H., Konig, T., Wahle, J., Schreckenberg, M.: Microscopic simulation of evacuation processes on passenger ships. In: Proceedings of Fourth International Conference on Cellular Automata for Research and Industry, Karlsruhe, Germany (October 2002)Google Scholar
  19. 19.
    Kolbe, T.H.: Representing and exchanging 3D city models with CityGML. In: Lee, J., Zlatanova, S. (eds.) 3D Geo-information Sciences, pp. 15–31. Springer, Berlin (2008)Google Scholar
  20. 20.
    OGC (Open Geospatial Consortium), http://www.opengeospatial.org
  21. 21.
    Park, I., Kim, H., Jun, C.: 2D-3D Hybrid Data Modeling for Fire Evacuation Simulation. In: ESRI international User Conference 2007, San Diego (2007), http://gis.esri.com/library/userconf/proc07/papers/papers/pap_1731.pdf
  22. 22.
    Penn, A., Hillier, B., Banister, D., Xu, J.: Configurational modelling of urban movement networks. Environment and Planning B-Planning & Design 25(1), 59–84 (1998)CrossRefGoogle Scholar
  23. 23.
  24. 24.
  25. 25.
    Schadschneider, A.: Cellular automaton approach to pedestrian dynamics - Theory. In: Schreckenberg, M., Sharma, S. (eds.) Pedestrian and Evacuation Dynamics, pp. 75–86. Springer, Berlin (2001)Google Scholar
  26. 26.
    Schreckenberg, M., Sharma, S.D. (eds.): Pedestrian and Evacuation Dynamics. Springer, Berlin (2001)MATHGoogle Scholar
  27. 27.
    Stadler, A., Kolbe, T.H.: Spatio-semantic coherence in the integration of 3D city models. In: Proceedings of 5th International ISPRS Symposium on Spatial Data Quality ISSDQ 2007 in Enschede (2007)Google Scholar
  28. 28.
    Stewart, J.Q., Warntz, W.: Physics of population distribution. Journal of Regional Science 1, 99–123 (1985)CrossRefGoogle Scholar
  29. 29.
    Stoter, J.E., van Oosterom, P.J.M.: Incorporating 3D geo-objects into a 2D geo-DBMS. In: ACSM-ASPRS 2002 (2002)Google Scholar
  30. 30.
    Stoter, J.E., Zlatanova, S.: Visualising and editing of 3D objects organised in a DBMS. In: Proceedings EUROSDR Workshop: Rendering and Visualisation, pp. 14–29 (2003)Google Scholar
  31. 31.
    Zlatanova, S.: 3D GIS for urban development, PhD thesis, Institute for Computer Graphics and Vision, Graz University of Technology, Austria, ITC, the Netherlands (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chulmin Jun
    • 1
  • Hyeyoung Kim
    • 1
  1. 1.Dept. of GeoinformaticsUniversity of SeoulSeoulKorea

Personalised recommendations