Biomimicry of Crowd Evacuation with a Slime Mould Cellular Automaton Model

  • Vicky S. Kalogeiton
  • Dim P. Papadopoulos
  • Ioannis P. Georgilas
  • Georgios Ch. Sirakoulis
  • Andrew I. Adamatzky
Part of the Studies in Computational Intelligence book series (SCI, volume 600)

Abstract

Evacuation is an imminent movement of people away from sources of danger. Evacuation in highly structured environments, e.g. building, requires advance planning and large-scale control. Finding a shortest path towards exit is a key for the prompt successful evacuation. Slime mould Physarum polycephalum is proven to be an efficient path solver: the living slime mould calculates optimal paths towards sources of attractants yet maximizes distances from repellents. The search strategy implemented by the slime mould is straightforward yet efficient. The slime mould develops may active traveling zones, or pseudopodia, which propagates along different, alternative, routes the pseudopodia close to the target loci became dominating and the pseudopodia propagating along less optimal routes decease. We adopt the slime mould’s strategy in a Cellular-Automaton (CA) model of a crowd evacuation. CA are massive-parallel computation tool capable for mimicking the Physarum’s behaviour. The model accounts for Physarum foraging process, the food diffusion, the organism’s growth, the creation of tubes for each organism, the selection of optimum path for each human and imitation movement of all humans at each time step towards near exit. To test the efficiency and robustness of the proposed CA model, several simulation scenarios were proposed proving that the model succeeds to reproduce sufficiently the Physarum’s inspiring behaviour.

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References

  1. 1.
    Adamatzky, A.: Physarum machine: Implementation of a kolmogorov-uspensky machine on a biological substrate. Parallel Processing Letters 17(4), 455–467 (2007)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Adamatzky, A.: Physarum machines: computers from slime mould, vol. 74. World Scientific, Singapore (2010)Google Scholar
  3. 3.
    Adamatzky, A.: Slime mold solves maze in one pass, assisted by gradient of chemo-attractants. IEEE Transactions on NanoBioscience 11(2), 131–134 (2012)CrossRefGoogle Scholar
  4. 4.
    Adamatzky, A.: Route 20, autobahn 7 and physarum polycephalum: Approximating longest roads in usa and germany with slime mould on 3d terrains. arXiv preprint arXiv:1211.0519. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics (2013) (in press)Google Scholar
  5. 5.
    Adamatzky, A., Jones, J.: Road planning with slime mould: if physarum built motorways it would route m6/m74 through newcastle. I. J. Bifurcation and Chaos 20(10), 3065–3084 (2010)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Adamatzky, A., Schumann, A.: Physarum spatial logic. New Mathematics and Natural Computation 07(03), 483–498 (2011)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Aubé, F., Shield, R.: Modeling the effect of leadership on crowd flow dynamics. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 601–611. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Bandini, S., Manzoni, S., Vizzari, G.: Situated cellular agents: A model to simulate crowding dynamics. IEICE Transactions on Information and Systems 87(3), 669–676 (2004)Google Scholar
  9. 9.
    Braun, A., Musse, S.R., de Oliveira, L.P.L., Bodmann, B.E.: Modeling individual behaviors in crowd simulation. In: 16th International Conference on Computer Animation and Social Agents, pp. 143–148. IEEE (2003)Google Scholar
  10. 10.
    Brogan, D.C., Hodgins, J.K.: Simulation level of detail for multiagent control. In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1, pp. 199–206. ACM (2002)Google Scholar
  11. 11.
    Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A: Statistical Mechanics and its Applications 295(3), 507–525 (2001)CrossRefMATHGoogle Scholar
  12. 12.
    Chenney, S.: Flow tiles. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 233–242. Eurographics Association (2004)Google Scholar
  13. 13.
    Chopard, B., Droz, M.: Cellular automata modeling of physical systems, vol. 122. Springer (1998)Google Scholar
  14. 14.
    Daoliang, Z., Lizhong, Y., Jian, L.: Exit dynamics of occupant evacuation in an emergency. Physica A: Statistical Mechanics and its Applications 363(2), 501–511 (2006)CrossRefGoogle Scholar
  15. 15.
    Feynman, R.P.: Simulating physics with computers. International Journal of Theoretical Physics 21(6), 467–488 (1982)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Georgoudas, I., Sirakoulis, G.C., Scordilis, E., Andreadis, I.: A cellular automaton simulation tool for modelling seismicity in the region of xanthi. Environmental Modelling & Software 22(10), 1455–1464 (2007)CrossRefGoogle Scholar
  17. 17.
    Georgoudas, I.G., Kyriakos, P., Sirakoulis, G.C., Andreadis, I.T.: An fpga implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields. Microprocessors and Microsystems 34(7), 285–300 (2010)CrossRefGoogle Scholar
  18. 18.
    Georgoudas, I.G., Sirakoulis, G.C., Andreadis, I.T.: A simulation tool for modelling pedestrian dynamics during evacuation of large areas. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations. IFIP, vol. 204, pp. 618–626. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Gunji, Y.P., Shirakawa, T., Niizato, T., Haruna, T.: Minimal model of a cell connecting amoebic motion and adaptive transport networks. Journal of Theoretical Biology 253(4), 659–667 (2008)CrossRefGoogle Scholar
  20. 20.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar
  21. 21.
    Henderson, L.: The statistics of crowd fluids. Nature 229, 381–383 (1971)CrossRefGoogle Scholar
  22. 22.
    Henein, C.M., White, T.: Agent-based modelling of forces in crowds. In: Davidsson, P., Logan, B., Takadama, K. (eds.) MABS 2004. LNCS (LNAI), vol. 3415, pp. 173–184. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Hoogendoorn, S.P.: Pedestrian travel behavior modeling. In: 10th International Conference on Travel Behavior Research, Lucerne (2003)Google Scholar
  24. 24.
    Jendrsczok, J., Ediger, P., Hoffmann, R.: A scalable configurable architecture for the massively parallel gca model. International Journal of Parallel, Emergent and Distributed Systems 24(4), 275–291 (2009)CrossRefMATHMathSciNetGoogle Scholar
  25. 25.
    Jian, L., Lizhong, Y., Daoliang, Z.: Simulation of bi-direction pedestrian movement in corridor. Physica A: Statistical Mechanics and its Applications 354, 619–628 (2005)CrossRefGoogle Scholar
  26. 26.
    Jones, J.: Approximating the behaviours of physarum polycephalum for the construction and minimisation of synthetic transport networks. In: Calude, C.S., Costa, J.F., Dershowitz, N., Freire, E., Rozenberg, G. (eds.) UC 2009. LNCS, vol. 5715, pp. 191–208. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Kalogeiton, V.S., Papadopoulos, D.P., Sirakoulis, G.C.: Hey physarum! can you perform slam? IJUC 10(4), 271–293 (2014)Google Scholar
  28. 28.
    Karafyllidis, I.: A model for the prediction of oil slick movement and spreading using cellular automata. Environment International 23(6), 839 – 850 (1997). doi:http://dx.doi.org/10.1016/S0160-41209700096-2
  29. 29.
    Karafyllidis, I., Thanailakis, A.: A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99(1), 87–97 (1997)CrossRefGoogle Scholar
  30. 30.
    Karafyllidis, I., Thanailakis, A.: A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99(1), 87–97 (1997), doi:http://dx.doi.org/10.1016/S0304-38009601942-4
  31. 31.
    Kirchner, A., Nishinari, K., Schadschneider, A.: Friction effects and clogging in a cellular automaton model for pedestrian dynamics. Physical Review E 67(5) 056, 122 (2003)Google Scholar
  32. 32.
    Lindzey, G.E., Aronson, E.E. (eds.): The handbook of social psychology. Addison-Wesley (1968)Google Scholar
  33. 33.
    Liu, Y., Zhang, Z., Gao, C., Wu, Y., Qian, T.: A physarum network evolution model based on IBTM. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part II. LNCS, vol. 7929, pp. 19–26. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  34. 34.
    Mardiris, V., Sirakoulis, G.C., Mizas, C., Karafyllidis, I., Thanailakis, A.: A cad system for modeling and simulation of computer networks using cellular automata. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews 38(2), 253–264 (2008)CrossRefGoogle Scholar
  35. 35.
    Milazzo, J.S., Rouphail, N.M., Hummer, J.E., Allen, D.P.: Effect of pedestrians on capacity of signalized intersections. Transportation Research Record: Journal of the Transportation Research Board 1646(1), 37–46 (1998)CrossRefGoogle Scholar
  36. 36.
    Musse, S.R., Thalmann, D.: Hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Computer Graphics 7(2), 152–164 (2001)CrossRefGoogle Scholar
  37. 37.
    Nakagaki, T., Yamada, H., Tóth, A.: Intelligence: Maze-solving by an amoeboid organism. Nature 407(6803), 470 (2000)CrossRefGoogle Scholar
  38. 38.
    Nakagaki, T., Yamada, H., Toth, A.: Path finding by tube morphogenesis in an amoeboid organism. Biophysical Chemistry 92(1), 47–52 (2001)CrossRefGoogle Scholar
  39. 39.
    Nakagaki, T., Yamada, H., Ueda, T.: Interaction between cell shape and contraction pattern in the i physarum plasmodium. Biophysical Chemistry 84(3), 195–204 (2000)CrossRefGoogle Scholar
  40. 40.
    Nishinari, K., Sugawara, K., Kazama, T., Schadschneider, A., Chowdhury, D.: Modelling of self-driven particles: Foraging ants and pedestrians. Physica A: Statistical Mechanics and its Applications 372(1), 132–141 (2006)CrossRefGoogle Scholar
  41. 41.
    Paris, S., Donikian, S.: Activity-driven populace: a cognitive approach to crowd simulation. IEEE Computer Graphics and Applications 29(4), 34–43 (2009)CrossRefGoogle Scholar
  42. 42.
    Perez, G.J., Tapang, G., Lim, M., Saloma, C.: Streaming, disruptive interference and power-law behavior in the exit dynamics of confined pedestrians. Physica A: Statistical Mechanics and its Applications 312(3), 609–618 (2002)CrossRefMATHGoogle Scholar
  43. 43.
    Schultz, M., Lehmann, S., Fricke, H.: A discrete microscopic model for pedestrian dynamics to manage emergency situations in airport terminals. In: Pedestrian and Evacuation Dynamics 2005, pp. 369–375. Springer (2007)Google Scholar
  44. 44.
    Schumann, A., Adamatzky, A.: Toward semantical model of reaction-diffusion computing. Kybernetes 38(9), 1518–1531 (2009)CrossRefMathSciNetGoogle Scholar
  45. 45.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graphical Models 69(5), 246–274 (2007)CrossRefGoogle Scholar
  46. 46.
    Shirakawa, T., Adamatzky, A., Gunji, Y.P., Miyake, Y.: On simultaneous construction of voronoi diagram and delaunay triangulation by physarum polycephalum. International Journal of Bifurcation and Chaos 19(09), 3109–3117 (2009)CrossRefGoogle Scholar
  47. 47.
    Shirakawa, T., Adamatzky, A., Gunji, Y.P., Miyake, Y.: On simultaneous construction of voronoi diagram and delaunay triangulation by physarum polycephalum. I. J. Bifurcation and Chaos 19(9), 3109–3117 (2009)CrossRefGoogle Scholar
  48. 48.
    Sirakoulis, G.C.: A tcad system for vlsi implementation of the cvd process using vhdl. Integration, the VLSI Journal 37(1), 63–81 (2004)CrossRefGoogle Scholar
  49. 49.
    Sirakoulis, G.C., Bandini, S. (eds.): ACRI 2012. LNCS, vol. 7495. Springer, Heidelberg (2012)Google Scholar
  50. 50.
    Sirakoulis, G.C., Karafyllidis, I., Thanailakis, A.: A cellular automaton model for the effects of population movement and vaccination on epidemic propagation. Ecological Modelling 133(3), 209–223 (2000)CrossRefGoogle Scholar
  51. 51.
    Sirakoulis, G.C., Karafyllidis, I., Thanailakis, A.: A cad system for the construction and vlsi implementation of cellular automata algorithms using vhdl. Microprocessors and Microsystems 27(8), 381–396 (2003)CrossRefGoogle Scholar
  52. 52.
    Spezzano, G., Talia, D., Di Gregorio, S., Rongo, R., Spataro, W.: A parallel cellular tool for interactive modeling and simulation. IEEE Computational Science & Engineering 3(3), 33–43 (1996)CrossRefGoogle Scholar
  53. 53.
    Stephenson, S.L., Stempen, H., Hall, I.: Myxomycetes: a handbook of slime molds. Timber press Portland, Oregon (1994)Google Scholar
  54. 54.
    Tero, A., Kobayashi, R., Nakagaki, T.: A mathematical model for adaptive transport network in path finding by true slime mold. Journal of Theoretical Biology 244(4), 553 (2007)CrossRefMathSciNetGoogle Scholar
  55. 55.
    Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for biologically inspired adaptive network design. Science 327(5964), 439–442 (2010)CrossRefMATHMathSciNetGoogle Scholar
  56. 56.
    Toffoli, T.: Cam: A high-performance cellular-automaton machine. Physica D: Nonlinear Phenomena 10(1), 195–204 (1984)CrossRefMathSciNetGoogle Scholar
  57. 57.
    Tsompanas, M.A.I., Sirakoulis, G.C.: Modeling and hardware implementation of an amoeba-like cellular automaton. Bioinspiration & Biomimetics 7(3), 036,013 (2012)Google Scholar
  58. 58.
    Tsuda, S., Aono, M., Gunji, Y.P.: Robust and emergent physarum logical-computing. Biosystems 73(1), 45–55 (2004)CrossRefGoogle Scholar
  59. 59.
    Varas, A., Cornejo, M., Mainemer, D., Toledo, B., Rogan, J., Munoz, V., Valdivia, J.: Cellular automaton model for evacuation process with obstacles. Physica A: Statistical Mechanics and its Applications 382(2), 631–642 (2007)CrossRefGoogle Scholar
  60. 60.
    Vichniac, G.Y.: Simulating physics with cellular automata. Physica D: Nonlinear Phenomena 10(1), 96–116 (1984)CrossRefMathSciNetGoogle Scholar
  61. 61.
    Vizzari, G., Manenti, L., Crociani, L.: Adaptive pedestrian behaviour for the preservation of group cohesion. Complex Adaptive Systems Modeling 1(1), 1–29 (2013)CrossRefGoogle Scholar
  62. 62.
    Von Neumann, J., Burks, A.W., et al.: Theory of self-reproducing automata. University of Illinois press Urbana (1966)Google Scholar
  63. 63.
    Weifeng, F., Lizhong, Y., Weicheng, F.: Simulation of bi-direction pedestrian movement using a cellular automata model. Physica A: Statistical Mechanics and its Applications 321(3), 633–640 (2003)CrossRefMATHGoogle Scholar
  64. 64.
    Wilding, N.B., Trew, A., Hawick, K., Pawley, G.: Scientific modeling with massively parallel simd computers. Proceedings of the IEEE 79(4), 574–585 (1991)CrossRefGoogle Scholar
  65. 65.
    Wolfram, S.: Theory and applications of cellular automata. Advanced Series on Complex Systems. World Scientific Publication, Singapore (1986)Google Scholar
  66. 66.
    Yang, L., Zhao, D., Li, J., Fang, T.: Simulation of the kin behavior in building occupant evacuation based on cellular automaton. Building and Environment 40(3), 411–415 (2005)CrossRefGoogle Scholar
  67. 67.
    Yu, Y., Song, W.: Cellular automaton simulation of pedestrian counter flow considering the surrounding environment. Physical Review E 75(4), 046,112 (2007)Google Scholar
  68. 68.
    Yuan, W., Tan, K.H.: An evacuation model using cellular automata. Physica A: Statistical Mechanics and its Applications 384(2), 549–566 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vicky S. Kalogeiton
    • 1
  • Dim P. Papadopoulos
    • 1
  • Ioannis P. Georgilas
    • 2
  • Georgios Ch. Sirakoulis
    • 1
  • Andrew I. Adamatzky
    • 2
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceOrestiadaGreece
  2. 2.Centre for Unconventional ComputingUniversity of the West of EnglandBristolUK

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