Environmental Effect on Egress Simulation

  • Samuel Rodriguez
  • Andrew Giese
  • Nancy M. Amato
  • Saied Zarrinmehr
  • Firas Al-Douri
  • Mark J. Clayton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7660)


Evacuation and egress simulations can be a useful tool for studying the effect of design decisions on the flow of agent movement. This type of simulation can be used to determine before hand the effect of design decisions and enable exploration of potential improvements. In this work, we study at how agent egress is affected by the environment in real world and large scale virtual environments and investigate metrics to analyze the flow. Our work differs from many evacuation systems in that we support grouping restrictions between agents (e.g., families or other social groups traveling together), and model scenarios with multiple modes of transportation with physically realistic dynamics (e.g., individuals walk from a building to their own cars and leave only when all people in the group arrive).


Evacuation Time Local Path Agent Movement Global Path Crowd Simulation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bekris, K.E., Tsianos, K., Kavraki, L.E.: A decentralized planner that guarantees the safety of communicating vehicles with complex dynamics that replan online. In: Proc. IEEE Int. Conf. Intel. Rob. Syst., IROS (2007)Google Scholar
  2. 2.
    van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-Body Collision Avoidance. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Robotics Research. STAR, vol. 70, pp. 3–19. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Blum, J., Eskandarian, A.: The impact of multi-modal transportation on the evacuation efficiency of building complexes. In: Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 702–707 (October 2004)Google Scholar
  4. 4.
    Christensen, K., Sasaki, Y.: Agent-based emergency evacuation simulation with individuals with disabilities in the population. Journal of Artificial Societies and Social Simulation 11(39) (2008)Google Scholar
  5. 5.
    Fang, Z., Li, Q., Li, Q., Han, L.D., Wang, D.: A proposed pedestrian waiting-time model for improving space-time use efficiency in stadium evacuation scenarios. Building and Environment 46, 1774–1784 (2011)CrossRefGoogle Scholar
  6. 6.
    Guy, S.J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., Manocha, D.: Pledestrians: A least-effort approach to crowd simulation. In: SCA 2010: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Eurographics Association, Madrid (2010)Google Scholar
  7. 7.
    Guy, S.J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., Dubey, P.: Clearpath: Highly parallel collision avoidance for multi-agent simulation. In: SCA 2009: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 177–187. Eurographics Association (2009)Google Scholar
  8. 8.
    Helbing, D., Buzna, L., Johansson, A., Werner, T.: Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 1–24 (2005)Google Scholar
  9. 9.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature, 487–490 (2000)Google Scholar
  10. 10.
    Johnson, C., Nilsen-Nygaard, L.: Extending the use of evacuation simulators to support counter terrorism. In: International Systems Safety Conference (2008)Google Scholar
  11. 11.
    Kapadia, M., Wang, M., Singh, S., Reinman, G., Faloutsos, P.: Scenario space: characterizing coverage, quality, and failure of steering algorithms. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2011, pp. 53–62. ACM, New York (2011), Google Scholar
  12. 12.
    Pauls, J.: The movement of people in buildings and design solutions for means of egress. Fire Technology 20, 27–47 (1984), CrossRefGoogle Scholar
  13. 13.
    Pelechano, N., Allbeck, J., Badler, N.: Controlling individual agents in high-density crowd simulation. In: ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2007)Google Scholar
  14. 14.
    Pelechano, N., Allbeck, J., Badler, N.: Virtual Crowds: Methods, Simulation, and Control. Synthesis Lectures on Computer Graphics and Animation. Morgan & Claypool (2008)Google Scholar
  15. 15.
    Pelechano, N., Badler, N.: Modeling crowd and trained leader behavior during building evacuation. IEEE Computer Graphics and Applications 26, 80–86 (2006)CrossRefGoogle Scholar
  16. 16.
    Pelechano, N., Malkawi, A.: Evacuation simulation models: Challenges in modeling high rise building evacuation with cellular automata approaches. Automation in Construction 17, 377–385 (2008)CrossRefGoogle Scholar
  17. 17.
    Pursals, S.C., Garzon, F.G.: Optimal building evacuation time considering evacuation routes. European Journal of Operational Research 192, 692–699 (2009)zbMATHCrossRefGoogle Scholar
  18. 18.
    Reynolds, C.W.: Steering behaviors for autonomous characters. In: Game Developers Conference (1999)Google Scholar
  19. 19.
    Rodriguez, S., Amato, N.M.: Utilizing roadmaps in evacuation planning. In: 24th Intern. Conf. on Computer Animation and Social Agents, CASA (2011); Intern. Journal of Virtual Reality, 67–73 (2011)Google Scholar
  20. 20.
    Singh, S., Kapadia, M., Faloutsos, P., Reinman, G.: Steerbench: a benchmark suite for evaluating steering behaviors. Comput. Animat. Virtual Worlds 20, 533–548 (2009)CrossRefGoogle Scholar
  21. 21.
    Singh, S., Kapadia, M., Hewlett, B., Reinman, G., Faloutsos, P.: A modular framework for adaptive agent-based steering. In: Symposium on Interactive 3D Graphics and Games, I3D 2011, pp. 141–150. ACM, New York (2011), Google Scholar
  22. 22.
    Thompson, P., Marchant, E.: A computer model for the evacuation of large building populations. Fire Safety Journal 24, 131–148 (1995)CrossRefGoogle Scholar
  23. 23.
    Torrens, P., Nara, A., Li, X., Zhu, H., Griffin, W., Brown, S.: An extensible simulation environment and movement metrics for testing walking behavior in agent-based models. Computers, Environment and Urban Systems 36(1), 1–17 (2012)zbMATHCrossRefGoogle Scholar
  24. 24.
    Zhang, X., Chang, G.: Optimal guidance of pedestrian-vehicle mixed flows in urban evacuation network. In: The 90th Annual Meeting of the Transportation Research Board (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Samuel Rodriguez
    • 1
  • Andrew Giese
    • 1
  • Nancy M. Amato
    • 1
  • Saied Zarrinmehr
    • 2
  • Firas Al-Douri
    • 3
  • Mark J. Clayton
    • 2
  1. 1.Parasol Lab, Dept. of Computer Science and EngineeringTexas A&M UniversityUSA
  2. 2.Dept. of Architecture, College of ArchitectureTexas A&M UniversityUSA
  3. 3.School of ArchitectureUniversity of Nevada, Las Vegas (UNLV)USA

Personalised recommendations