Advertisement

City Scale Evacuation: A High-Performance Multi-agent Simulation Framework

  • Kashif ZiaEmail author
  • Alois FerschaEmail author
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Understanding the dynamics of urban evacuation systems – due to disasters induced by forces of nature like flooding or tsunamis, terrorism or nuclear power plant accidents – has elicited massive interest over the past years. To perform a simulation for a socio-technical scenario; a typical landscape towards which the modern day cities are increasingly heading to; more recent multi-agent based methodology has increasingly being adopted. In this contribution simulation models of social agents at massive scale are presented. High performance simulation experiments are conducted for the analysis of realistic evacuation models at the level of large cities (\( {10^6}-{10^8} \)). Variations of demographics and the morphology of cities together with population densities, mobility patterns, individual decision making and agent interactions are analysed.

Keywords

Geographic Information System Mobile Agent Disaster Risk Cellular Automaton Cellular Automaton Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Albala-Bertrand, J.M.: Urban disasters and globalization. In: Building Safer Cities: The Future of Disaster Risk, pp. 75–82. World Bank Publications, Washington, DC (2003)Google Scholar
  2. 2.
    Kreimer, A., Arnold, M., Carlin, A.: Building Safer Cities: The Future of Disaster Risk. World Bank Publications, Washington, DC (2003)Google Scholar
  3. 3.
    Benson, C., Clay, E.: Disasters, vulnerability and the global economy. In: Building Safer Cities: The Future of Disaster Risk, pp. 3–32. World Bank Publications, Washington, DC (2003)Google Scholar
  4. 4.
    Andersen, T.J.: Globalization and natural disasters: an integrative risk management perspective. In: Building Safer Cities: The Future of Disaster Risk, pp. 57–74. World Bank Publications, Washington, DC (2003)Google Scholar
  5. 5.
    Kunreuther, H.: Interdependent disaster risks: the need for public-private partnerships. In: Building Safer Cities: The Future of Disaster Risk, pp. 83–86. World Bank Publications, Washington, DC (2003)Google Scholar
  6. 6.
    Bigio, A.G.: Cities and climate change. In: Building Safer Cities: The Future of Disaster risk, pp. 91–100. World Bank Publications, Washington, DC (2003)Google Scholar
  7. 7.
    Klein, R.J.T., Nicholls, R.J., Thomalla, F.: The resilience of coastal megacities to weather-related hazards. In: Building Safer Cities: The Future of Disaster Risk, pp. 101–121. World Bank Publications, Washington, DC (2003)Google Scholar
  8. 8.
    Wisner, B.: Disaster risk reduction in megacities: making the most of human and social capital. In: Building Safer Cities: The Future of Disaster Risk, pp. 181–196. World Bank Publications, Washington, DC (2003)Google Scholar
  9. 9.
    Robert, B., Sabourin, J.P., Glaus, M., Petit, F., Senay, M.H.: A new structural approach for the study of domino effects between life support networks. In: Building Safer Cities: The Future of Disaster Risk, pp. 245–272. World Bank Publications, Washington, DC (2003)Google Scholar
  10. 10.
    Wenzel, F., Bendimerad, F., Sinha, R.: Megacities – megarisks. Nat. Hazards 42(3), 481–491 (2007)CrossRefGoogle Scholar
  11. 11.
    Etkin, D., Malkin-Dubins. L.: Disasters and the in-between city. In: In-Between Infrastructure: Urban Connectivity in an Age of Vulnerability, pp. 49–66. Praxis (e) Press, Kelowna http://www.praxis-epress.org/availablebooks/inbetween.html (2011)
  12. 12.
    Mitchell, J.K.: Crucibles of Hazard: Mega-Cities and Disasters in Transition. United Nations University Press, New York (1999)Google Scholar
  13. 13.
    Huq, S., Kovats, S., Reid, H., Satterthwaite, D.: Editorial: reducing risks to cities from disasters and climate change. Environ. Urban 19(1), 3–15 (2007)CrossRefGoogle Scholar
  14. 14.
    Wiki: Urban typology: definitionGoogle Scholar
  15. 15.
    Shane, D.G.: Transcending type: designing for urban complexity. Archit. Des. 81(1), 128–134 (2011)Google Scholar
  16. 16.
    Rossi, A.: The Architecture of the City, Translation of “L’architettura della citta”. Oppositions book. The MIT Press, Cambridge/London (1984)Google Scholar
  17. 17.
    Schmidt-Thomé, P.: ESPON project 1.3. 1–the spatial effects and management of natural and technological hazards in general and in relation to climate change. Technical report, Espoo, Geo-logical Survey of Finland, (2005)Google Scholar
  18. 18.
    van Winden, W., van den Berg, L., Peter, P.: European cities in the knowledge economy: towards a typology. Urban Stud. 44, 525–549 (2007)CrossRefGoogle Scholar
  19. 19.
    Smart cities projectGoogle Scholar
  20. 20.
    Baron, R., Zintel, M., Doyon, A., Van Audenhove, F., Bassanino, A.: The future of urban mobility. Technical report, ADL, Prism, (2011)Google Scholar
  21. 21.
    Potentials for polycentric development in Europe: The ESPON 1.1.1 project report. Technical report, Nordic Center for Spatial Development (2005)Google Scholar
  22. 22.
    Gilbert, G.N.: Agent-Based Models. Sage, Los Angeles (2008)Google Scholar
  23. 23.
    Ferscha, A.: Parallel and distributed simulation of discrete event systems. In: Parallel and Distributed Computing Handbook, pp. 1003–1041. McGraw-Hill, New York (1996)Google Scholar
  24. 24.
    Lynch, K.: The Image of the City. MIT Press, Cambridge (1971)Google Scholar
  25. 25.
    Macy, M.W., Willer, R.: From factors to actors: computational sociology and agent-based modeling. Annu. Rev. Sociol. 28, 143–166 (2002)CrossRefGoogle Scholar
  26. 26.
    Kauffman, S.A.: At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford University Press, New York (1995)Google Scholar
  27. 27.
    Simon, H.A.: The Sciences of the Artificial. The MIT Press, Cambridge (1996)Google Scholar
  28. 28.
    Holland, J.H.: Hidden Order: How Adaptation Builds Complexity. Basic Books, New York (1996)Google Scholar
  29. 29.
    Gilbert, N.: A simulation of the structure of academic science. Sociol. Res. Online. 2 (1997)Google Scholar
  30. 30.
    Axelrod, R.M.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)Google Scholar
  31. 31.
    Bandura, A., Ross, D., Ross, S.A.: Transmission of aggression through imitation of aggressive models. J. Abnorm. Soc. Psychol. 63(3), 575 (1961)CrossRefGoogle Scholar
  32. 32.
    Reed, M., Evely, A.C., Cundill, G., Fazey, I.R.A., Glass, J., Laing, A., Newig, J., Parrish, B., Prell, C., Raymond, C., et al.: What is social learning? Ecol. Soc. 15(4) (2010)Google Scholar
  33. 33.
    Nolfi, S., Floreano, D.: Learning and evolution. Auton. Robot. 7(1), 98–113 (1999)CrossRefGoogle Scholar
  34. 34.
    Hamilton, W.D.: The evolution of altruistic behavior. Am. Nat. 97(896), 354–356 (1963)CrossRefGoogle Scholar
  35. 35.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)CrossRefGoogle Scholar
  36. 36.
    Kahneman, D.: Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2003)CrossRefGoogle Scholar
  37. 37.
    Latané, B.: Dynamic social impact: the creation of culture by communication. J. Commun. 46(4), 13–25 (1996)CrossRefGoogle Scholar
  38. 38.
    Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211(4489), 1390–1396 (1981)MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Raub, W., Buskens, V., van Assen, M.A.L.M.: Micro-macro links and microfoundations in sociology. J. Math. Sociol. 35(1–3), 1–25 (2011)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Dauber, K., Pan, X., Han, C.S., Law, K.H.: Human and social behavior in computational modeling and analysis of egress. Autom. Constr. 15(4), 448–461 (2006)CrossRefGoogle Scholar
  41. 41.
    Pan, X.S., Han, C.S., Dauber, K., Law, K.H.: A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI. Soc. 22, 113–132 (2007)CrossRefGoogle Scholar
  42. 42.
    Bryan, J.: Human behavior and fire. In: Fire Protection Handbook, vol. 1, 19th edn, pp. 4.3–4.32. National Fire Protection Association, Quincy (2003)Google Scholar
  43. 43.
    Zia, K., Ferscha, A.: Lifebelt: crowd evacuation based on vibro-tactile guidance. IEEE Pervasive Comput. 9(4), 33–42 (2010)CrossRefGoogle Scholar
  44. 44.
    Ferscha, A., Zia, K.: On the efficiency of lifebelt based crowd evacuation. In: 15th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2009), IEEE Computer Society Press, Singapore, 25–28 Oct 2009Google Scholar
  45. 45.
    Zia, K., Ferscha, A.: A simulation study of exit choice based on effective throughput of an exit area in a multi-exit evacuation situation. In: th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2009), Singapore, IEEE Computer Society Press, 25–28 Oct 2009Google Scholar
  46. 46.
    Ferscha, A., Zia, K.: Self-organized evacuation based on lifebelt. In: Proceedings of the 3rd International Workshop on Self-Organizing Systems (IWSOS 2009), Springer-Verlag, Lecture Notes in Computer Science (LNCS), (2009)Google Scholar
  47. 47.
    Le Bon, G.: The Crowd: A Study of the Popular Mind. Unwin, London (1908)CrossRefGoogle Scholar
  48. 48.
    Lovas, G.C.: Modeling and simulation of pedestrian traffic flow. Transport. Res. 28(6), 429–443 (1994)CrossRefGoogle Scholar
  49. 49.
    Kleinberg, J., Kempe, D., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, ACM Press, New York, pp 137–146 (2003)Google Scholar
  50. 50.
    Li, C.-T., Hsieh, H.-P., Kuo, T.-T., Lin, S.-D.: Sociocrowd: a social-network-based framework for crowd simulation. In: ACM SIGGRAPH 2010 Posters, ACM, New York (2010)Google Scholar
  51. 51.
    Kirchner, A., Schadschneider, A., Nishinari, K.: CA approach to collective phenomena Cellular Automata. In: proceedings of 5th International Conference on Cellular Automata for Research and Industry, ACRI 2002, Geneva, 9–11 Oct 2002. Chapter pedestrian dynamics, pp. 239–248. Lecture notes in computer science, Springer, (2002)Google Scholar
  52. 52.
    Farkas, I.J., Molnar, P., Helbing, D., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation simulations. In: Pedestrian and Evacuation Dynamics, pp. 21–58. Springer, Berlin/New York (2002)Google Scholar
  53. 53.
    Hoogendoorn, S.P.: Pedestrian travel behavior modeling. In: Proceedings of Travel Behavior Research, Elsevier, Lucerne 10–15 Aug 2003Google Scholar
  54. 54.
    Penn, A., Turner, A.: Space syntax based agent simulation. In: Pedestrian and Evacuation Dynamics, pp. 99–114. Springer, Berlin/New York (2002)Google Scholar
  55. 55.
    Borgers, A., Timmermans, H.: A model of pedestrian route choice and demand for retail facilities within inner-city shopping areas. Geogr. Anal. 18(2), 115–128 (1986)CrossRefGoogle Scholar
  56. 56.
    Gardner, M.: The fantastic combinations of John Conway’s new solitaire game “life”. Sci. Am. 223, 120–123 (1970)CrossRefGoogle Scholar
  57. 57.
    Dijkstra, J., Timmermans, H.J.P., Jessurun, A.J.: A multi-agent cellular automata system for visualizing simulated pedestrian activity. In: Cellular Automata for Research and Industry, pp. 29–36. Springer-Verlag, Berlin (2000)Google Scholar
  58. 58.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies Social Science from the Bottom Up. MIT Press, Cambridge (1996)Google Scholar
  59. 59.
    Kirchner, A., Namazi, A., Nishinari, K., Schadschneider, A.: Role of conflicts in the floor field cellular automaton model for pedestrian dynamics. In: Proceedings of 2nd International Conference on Pedestrians and Evacuation Dynamics. (PED), pp. 51–62. London, (2003)Google Scholar
  60. 60.
    Uri Wilensky.: Netlogo modeling environment online, last Retrieved, 23 Oct 23 2009. http://ccl.northwestern.edu/netlogo
  61. 61.
    North, M.J., Collier, N.T., Vos, J.R.: Experiences creating three implementations of the repast agent modeling toolkit. ACM Trans. Model. Comput. Simul. 16, 1–25 (2006)CrossRefGoogle Scholar
  62. 62.
    Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: Mason: a multiagent simulation environment. Simulation 81, 517–527 (2005)CrossRefGoogle Scholar
  63. 63.
    Kretz, T., Schreckenberg, M.: Moore and more and symmetry. In: Pedestrian and Evacuation Dynamics 2005, Springer, pp. 297–308. (2007)Google Scholar
  64. 64.
    Macal, C., North, M.: Tutorial on agent-based modelling and simulation. J. Simul. 4, 151–162 (2010)CrossRefGoogle Scholar
  65. 65.
    Almeida, C., Batty, M., Monteiro, A., Camara, G., Soares-Filho, B., Cerqueira, G., Pennachin, C.: Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Comput. Environ. Urban Syst. 27(5), 481–509 (2003)CrossRefGoogle Scholar
  66. 66.
    Almeida, C.M., Gleriani, J.M., Castejon, E.F., Soares-Filho, B.S.: Using neural networks and cellular automata for modelling intra-urban land-use dynamics. Inter. J. Geogr. Info. Sci. 22, 943–963 (2008)CrossRefGoogle Scholar
  67. 67.
    Yassemi, S., Dragicevic, S., Schmidt, M.: Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecol. Model. 210(1), Elsevier, 71–84 (2008)Google Scholar
  68. 68.
    Sun, T., Wang, J.: A traffic cellular automata model based on road network grids and its spatial and temporal resolution’s influences on simulation. Simul. Model. Pract. Theory 15(7), 864–878 (2007)CrossRefGoogle Scholar
  69. 69.
    White, R., Engelen, G.: High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput. Environ. Urban Syst. 24(5), 383–s400 (2000)CrossRefGoogle Scholar
  70. 70.
    Zhang, X., Chang, G.L.: Optimal control strategies for massive vehicular-pedestrian mixed flows in the evacuation zone. In: The 89th transportation annual meeting of the Transportation Research Board, January 2010. Washington, DC (2010)Google Scholar
  71. 71.
    Blue, V.J., Adler, J.L.: Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transport. Res. B 35(3), 293–312 (2001)CrossRefGoogle Scholar
  72. 72.
    Nick Collier.: Repast HPC Manual. Technical report, 23 Nov 2010. p. 44Google Scholar
  73. 73.
    Moser, D., Riener, A., Zia, K., Ferscha, A.: Comparing parallel simulation of social agents using Cilk and OpenCL. In: Proceedings of 15th International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2011), Salford, p. 10. IEEE CS Press, Sept 2011Google Scholar
  74. 74.
    Riener, A., Sharpanskykh, A., Ferscha, A., Zia, K.: Potential of social modelling in socio-technical systems. In: Submitted FET 2011. (2011)Google Scholar
  75. 75.
    Sharpanskykh, A., Zia, K.: Grouping behaviour in ami-enabled crowd evacuation. In: Proceedings of the 2nd international symposium on ambient intelligence, ISAmI'10. Springer, Salamanca, 6–8 Apr 2011 (2011)Google Scholar
  76. 76.
    Zia, K., Riener, A., Ferscha, A., Sharpanskykh, A.: Evacuation simulation based on cognitive decision making model in a socio-technical system. In: th International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2011), Salford, p. 10. IEEE CS Press, Sept 2011Google Scholar
  77. 77.
    Li, X., Zhang, X., Yeh, A., Liu, X.: Parallel cellular automata for large-scale urban simulation using load-balancing techniques. Int. J. Geogr. Info. Sci. 24, 803–820 (2010)CrossRefGoogle Scholar
  78. 78.
    Collier, N.T., North, M.J.: Repast SC++: a platform for large-scale agent-based modeling. In: Large-Scale Computing Techniques for Complex System Simulations. Wiley, Chicester (2011)Google Scholar
  79. 79.
    Ferscha, A., Zia, K.: LifeBelt: silent directional guidance for crowd evacuation. In: Proceedings of the 13th International Symposium on Wearable Computers (ISWC09). IEEE Computer Society Press, Linz, 4–7 Sept 2009Google Scholar
  80. 80.
    Zia, K., Ferscha, A., Riener, A., Wirz, M., Roggen, D., Kloch, K., Lukowicz, P.: Scenario based modeling for very large scale simulations. In: th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2010), Fairfax, p. 8. IEEE Computer Society Press, Oct 2010Google Scholar
  81. 81.
    Sharpanskykh, A., Zia, K.: Emotional decision making in large crowds. In: Proceedings of the 10th international conference on practical applications of agents and multi-agent systems, PAAMS'12. Springer, University of Salamanca 22–24 May 2012 (2012)Google Scholar
  82. 82.
    Murphy, J.T.: Computational social science and high performance computing: a case study of a simple model at large scales. Technical report, Argonne National Laboratory, Argonne, Illinois Sept 2011Google Scholar
  83. 83.
    Zia, K., Riener, A., Farrahi, K., Ferscha, A.: A new opportunity to urban evacuation analysis: very large scale simulations of social agent systems in repast HPC. In: Principles of Advanced and Distributed Simulation (PADS), 2012 ACM/IEEE/SCS 26th Workshop on, IEEE Computer Society, Washington, DC pp. 233–242 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.JKU LinzInstitute for Pervasive ComputingLinzAustria

Personalised recommendations