Computational Visual Media

, Volume 3, Issue 3, pp 243–261 | Cite as

Optimized evacuation route based on crowd simulation

  • Sai-Keung Wong
  • Yu-Shuen Wang
  • Pao-Kun Tang
  • Tsung-Yu Tsai
Open Access
Research Article


An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed, and determines the routes in a hierarchical manner. This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results, with visualizations, demonstrate the feasibility of our evacuation route optimization method.


crowd simulation evacuation path planning optimization 



The authors thank the reviewers for their constructive comments. This work was supported in part by “the Ministry of Science and Technology of Taiwan” under Grant MOST 102-2221-E-009-083-MY3, Grant MOST 103-2221-E-009-122-MY3, and Grant MOST 104-2221-E-009-051-MY3.


  1. [1]
    Hamacher, H. W.; Tjandra, S. A. Mathematical modelling of evacuation problems: A state of the art. Fraunhofer-Institut für Techno-und Wirtschaftsmathematik, 2001.zbMATHGoogle Scholar
  2. [2]
    Wong, S.-K.; Wang, Y.-S.; Tang, P.-K.; Tsai, T.-Y. Optimized route for crowd evacuation. In: Pacific Graphics Short Papers. Grinspun, E.; Bickel, B.; Dobashi, Y. Eds. The Eurographics Association, 2016.Google Scholar
  3. [3]
    Schadschneider, A.; Klingsch, W.; Klüpfel, H.; Kretz, T.; Rogsch, C.; Seyfried, A. Evacuation dynamics: Empirical results, modeling and applications. In: Encyclopedia of Complexity and Systems Science. Meyers, R. A. Ed. Springer New York, 3142–3176, 2009.CrossRefGoogle Scholar
  4. [4]
    Dreßsler, D.; Groß, M.; Kappmeier, J.-P.; Kelter, T.; Kulbatzki, J.; Plümpe, D.; Schlechter, G.; Schmidt, M.; Skutella, M.; Temme, S. On the use of network flow techniques for assigning evacuees to exits. Procedia Engineering Vol. 3, 205–215, 2010.CrossRefGoogle Scholar
  5. [5]
    Hadzic, T.; Brown, N.; Sreenan, C. J. Real-time pedestrian evacuation planning during emergency. In: Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 597–604, 2011.Google Scholar
  6. [6]
    Abdelghany, A.; Abdelghany, K.; Mahmassani, H.; Alhalabi, W. Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities. European Journal of Operational Research Vol. 237, No. 3, 1105–1118, 2014.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    Wang, H.-R.; Chen, Q.-G.; Yan, J.-B.; Yuan, Z.; Liang, D. Emergency guidance evacuation in fire scene based on pathfinder. In: Proceedings of the 7th International Conference on Intelligent Computation Technology and Automation, 226–230, 2014.Google Scholar
  8. [8]
    Desmet, A.; Gelenbe, E. Capacity based evacuation with dynamic exit signs. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, 332–337, 2014.Google Scholar
  9. [9]
    Tang, H.; Elalouf, A.; Levner, E.; Cheng, T. C. E. Efficient computation of evacuation routes on a three-dimensional geometric network. Computers & Industrial Engineering Vol. 76, 231–242, 2014.CrossRefGoogle Scholar
  10. [10]
    Berseth, G.; Usman, M.; Haworth, B.; Kapadia, M.; Faloutsos, P. Environment optimization for crowd evacuation. Computer Animation and Virtual Worlds Vol. 26, Nos. 3–4, 377–386, 2015.CrossRefGoogle Scholar
  11. [11]
    Haworth, B.; Usman, M.; Berseth, G.; Kapadia, M.; Faloutsos, P. Code: Crowd optimized design of environments. In: Proceedings of the 29th International Conference on Computer Animation and Social Agents, 2016.Google Scholar
  12. [12]
    Haworth, B.; Usman, M.; Berseth, G.; Kapadia, M.; Faloutsos, P. Evaluating and optimizing level of service for crowd evacuations. In: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games, 91–96, 2015.CrossRefGoogle Scholar
  13. [13]
    Moussaïd, M.; Kapadia, M.; Thrash, T.; Sumner, R. W.; Gross, M.; Helbing, D.; Hölscher, C. Crowd behaviour during high-stress evacuations in an immersive virtual environment. Journal of the Royal Society Interface Vol. 13, No. 122, 20160414, 2016.CrossRefGoogle Scholar
  14. [14]
    Thompson, P. A.; Marchant, E. W. A computer model for the evacuation of large building populations. Fire Safety Journal Vol. 24, No. 2, 131–148, 1995.CrossRefGoogle Scholar
  15. [15]
    Lovas, G. G. On the importance of building evacuation system components. IEEE Transactions on Engineering Management Vol. 45, No. 2, 181–191, 1998.CrossRefGoogle Scholar
  16. [16]
    Pelechano, N.; Badler, N. I. Modeling crowd and trained leader behavior during building evacuation. IEEE Computer Graphics and Applications Vol. 26, No. 6, 80–86, 2006.CrossRefGoogle Scholar
  17. [17]
    Ma, Y.; Yuen, R. K. K.; Lee, E. W. M. Effective leadership for crowd evacuation. Physica A: Statistical Mechanics and its Applications Vol. 450, 333–341, 2016.CrossRefGoogle Scholar
  18. [18]
    Dimakis, N.; Filippoupolitis, A.; Gelenbe, E. Distributed building evacuation simulator for smart emergency management. The Computer Journal Vol. 53, No. 9, 1384–1400, 2009.CrossRefGoogle Scholar
  19. [19]
    Rodriguez, S.; Amato, N. Behavior-based evacuation planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, 350–355, 2010.Google Scholar
  20. [20]
    Tsai, J.; Fridman, N.; Bowring, E.; Brown, M.; Epstein, S.; Kaminka, G.; Marsella, S.; Ogden, A.; Rika, I.; Sheel, A.; Taylor, M. E.; Wang, X.; Zilka, A.; Tambe, M. ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Vol. 2, 457–464, 2011.Google Scholar
  21. [21]
    Kretz, T.; Grosse, A.; Hengst, S.; Kautzsch, L.; Pohlmann, A.; Vortisch, P. Quickest paths in simulations of pedestrians. Advances in Complex Systems Vol. 14, No. 5, 733–759, 2011.CrossRefGoogle Scholar
  22. [22]
    Inoue, Y.; Sashima, A.; Ikeda, T.; Kurumatani, K. Indoor emergency evacuation service on autonomous navigation system using mobile phone. In: Proceedings of the 2nd International Symposium on Universal Communication, 79–85, 2008.Google Scholar
  23. [23]
    Chen, C.-Y. The design of smart building evacuation system. International Journal of Control Theory and Applications Vol. 5, No. 1, 73–80, 2012.Google Scholar
  24. [24]
    Helbing, D.; Molnáar, P. Social force model for pedestrian dynamics. Physical Review E Vol. 51, 4282–4286, 1995.CrossRefGoogle Scholar
  25. [25]
    Van den Berg, J.; Lin, M.; Manocha, D. Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceeding of the IEEE International Conference on Robotics and Automation, 1928–1935, 2008.Google Scholar
  26. [26]
    Karamouzas, I.; Heil, P.; van Beek, P.; Overmars, M. H. A predictive collision avoidance model for pedestrian simulation. In: Motion in Games. Egges, A.; Geraerts, R.; Overmars, M. Eds. Springer-Verlag Berlin Heidelberg, 41–52, 2009.CrossRefGoogle Scholar
  27. [27]
    Guy, S. J.; Chhugani, J.; Curtis, S.; Dubey, P.; Lin, M.; Manocha, D. PLEdestrians: A least-effort approach to crowd simulation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 119–128, 2010.Google Scholar
  28. [28]
    Curtis, S.; Zafar, B.; Gutub, A.; Manocha, D. Right of way. The Visual Computer Vol. 29, No. 12, 1277–1292, 2013.CrossRefGoogle Scholar
  29. [29]
    Johansson, A.; Helbing, D.; Shukla, P. K. Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems Vol. 10, No. supp02, 271–288, 2007.Google Scholar
  30. [30]
    Lee, K. H.; Choi, M. G.; Hong, Q.; Lee, J. Group behavior from video: A data-driven approach to crowd simulation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 109–118, 2007.Google Scholar
  31. [31]
    Durupınar, F.; Güdükbay, U.; Aman, A.; Badler, N. I. Psychological parameters for crowd simulation: From audiences to mobs. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 9, 2145–2159, 2016.CrossRefGoogle Scholar
  32. [32]
    Boatright, C. D.; Kapadia, M.; Shapira, J. M.; Badler, N. I. Generating a multiplicity of policies for agent steering in crowd simulation. Computer Animation and Virtual Worlds Vol. 26, No. 5, 483–494, 2015.CrossRefGoogle Scholar
  33. [33]
    Li, F.-S.; Wong, S.-K. Animating agents based on radial view in crowd simulation. In: Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology, 101–109, 2016.CrossRefGoogle Scholar
  34. [34]
    Kapadia, M.; Pelechano, N.; Allbeck, J.; Badler, N. Virtual Crowds: Steps toward Behavioral Realism. Morgan & Claypool Publishers, 2015.Google Scholar
  35. [35]
    Geraerts, R.; Overmars, M. H. The corridor map method: A general framework for real-time highquality path planning. Computer Animation & Virtual Worlds Vol. 18, No. 2, 107–119, 2007.CrossRefGoogle Scholar
  36. [36]
    Van Toll W. G.; Cook IV, A. F.; Geraerts, R. Real-time density-based crowd simulation. Computer Animation & Virtual Worlds Vol. 23, No. 1, 59–69, 2012.CrossRefGoogle Scholar
  37. [37]
    Mekni, M. Hierarchical path planning for situated agents in informed virtual geographic environments. In: Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, Article No. 30, 2010.Google Scholar
  38. [38]
    Kallmann, M.; Bieri, H.; Thalmann, D. Fully dynamic constrained delaunay triangulations. In: Geometric Modeling for Scientific Visualization. Brunnett, G.; Hamann, B.; Müller, H.; Linsen, L. Eds. Springer-Verlag Berlin Heidelberg, 241–257, 2004.CrossRefGoogle Scholar
  39. [39]
    Pettré, J.; Laumond, J. P.; Thalmann, D. A navigation graph for real-time crowd animation on multilayered and uneven terrain. In: Proceedings of the 1st International Workshop on Crowd Simulation, Vol. 43, No. 44, 194, 2005.Google Scholar
  40. [40]
    Bayazit, O. B.; Lien, J. M.; Amato, N. M. Better group behaviors in complex environments using global roadmaps. Artificial Life 8 Vol. 8, 362, 2003.Google Scholar
  41. [41]
    Patil, S.; van Den Berg, J.; Curtis, S.; Lin, M. C.; Manocha, D. Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 2, 244–254, 2011.CrossRefGoogle Scholar
  42. [42]
    Wong, S.-K.; Tang, P.-K.; Li, F.-S.; Wang, Z.-M.; Yu, S.-T. Guidance path scheduling using particle swarm optimization in crowd simulation. Computer Animation & Virtual Worlds Vol. 26, Nos. 3–4, 387–395, 2015.CrossRefGoogle Scholar
  43. [43]
    Müller, M.; Heidelberger, B.; Hennix, M.; Ratcliff, J. Position based dynamics. Journal of Visual Communication and Image Representation Vol. 18, No. 2, 109–118, 2007.CrossRefGoogle Scholar
  44. [44]
    Donelson, S. M.; Gordon, C. C. 1995 matched anthropometric database of U.S. marine corps personnel: Summary statistics. Technical Report. Geo-Centers INC Newton Centre MA, 1996.Google Scholar
  45. [45]
    Aspelin, K. Establishing pedestrian walking speeds. Portland State University, 5–25, 2005.Google Scholar
  46. [46]
    TranSafety Inc. Study compares older and younger pedestrian walking speeds. Road Management & Engineering Journal, 1997.Google Scholar
  47. [47]
    Bera, A.; Kim, S.; Manocha, D. Online parameter learning for data-driven crowd simulation and content generation. Computers & Graphics Vol. 55, 68–79, 2016.CrossRefGoogle Scholar
  48. [48]
    Yang, L.; Zhu, K.; Liu, S. Cellular automata evacuation model considering information transfer in building with obstacles. In: Pedestrain Dynamics and Evacuation. Peacock, R. D.; Kuligowski, E.; Averill, J. D. Eds. Springer Science+Business Media Springer, 317–326 2011.CrossRefGoogle Scholar
  49. [49]
    Hamacher, H.; Heller, S.; Klein, W.; Köster, G.; Ruzika, S. A sandwich approach for evacuation time bounds. In: Pedestrian and Evacuation Dynamics. Peacock, R.; Kuligowski, E.; Averill, J. Eds. Boston: Springer, 503–513, 2011.CrossRefGoogle Scholar
  50. [50]
    Zhong, J.; Cai, W.; Luo, L. Crowd evacuation planning using cartesian genetic programming and agent-based crowd modeling. In: Proceedings of the Winter Simulation Conference, 127–138, 2015.Google Scholar
  51. [51]
    Lin, W.-C.; Wong, S.-K.; Li, C.-H.; Tseng, R. Generating believable mixed-traffic animation. IEEE Transactions on Intelligent Transportation Systems Vol. 17, No. 11, 3171–3183, 2016.CrossRefGoogle Scholar
  52. [52]
    Feng, T.; Yu, L.-F.; Yeung, S.-K.; Yin, K.; Zhou, K. Crowd-driven mid-scale layout design. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 132, 2016.Google Scholar

Copyright information

© The Author(s) 2017

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from To submit a manuscript, please go to

Authors and Affiliations

  • Sai-Keung Wong
    • 1
  • Yu-Shuen Wang
    • 1
  • Pao-Kun Tang
    • 1
  • Tsung-Yu Tsai
    • 1
  1. 1.“National Chiao Tung University”TaiwanChina

Personalised recommendations