Skip to main content

Advertisement

SpringerLink
Log in

Crowd Simulation and Its Applications: Recent Advances

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation, pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Thalmann D, Musse S. Crowd Simulation (1st edition). Springer-Verlag, 2007.

  2. Hawe G I, Coates G, Wilson D T et al. Agent-based simulation for large-scale emergency response: A survey of usage and implementation. ACM Computing Surveys (CSUR), 2012, 45(1): Article No.8.

  3. Schelhorn T, O’Sullivan D, Haklay M et al. STREETS: An agent-based pedestrian model. In Proc. the 6th International Conference on Computers in Urban Planning and Urban Management, April 1999.

  4. Penn A, Turner A. Space syntax based agent simulation. In Proc. the 1st International Conference on Pedestrian and Evacuation Dynamics, 2001, pp.99–114.

  5. Turner A, Penn A. Encoding natural movement as an agent-based system: An investigation into human pedestrian behaviour in the built environment. Environment and Planning B: Planning and Design, 2002, 29(4): 473–490.

    Article  Google Scholar 

  6. Thompson P A, Marchant E W. A computer model for the evacuation of large building populations. Fire Safety Journal, 1995, 24(2): 131–148.

    Article  Google Scholar 

  7. He G Q, Yang Y, Chen Z H et al. A review of behavior mechanisms and crowd evacuation animation in emergency exercises. Journal of Zhejiang University SCIENCE C, 2013, 14(7): 477–485.

    Article  MathSciNet  Google Scholar 

  8. Helbing D, Farkas I, Vicsek T. Simulating dynamical features of escape panic. Nature, 2000, 407: 487–490.

    Article  Google Scholar 

  9. Bohannon J. Directing the herd: Crowds and the science of evacuation. Science, 2005, 310(5746): 219–221.

    Article  Google Scholar 

  10. Henry J, Shum H, Komura T. Interactive formation control in complex environments. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(2): 211–222.

    Article  Google Scholar 

  11. Wang X, Zhou L, Deng Z et al. Flock morphing animation. Computer Animation and Virtual Worlds, 2014, 25(3/4): 353–362.

    Google Scholar 

  12. Henderson L. The statistics of crowd fluids. Nature, 1971, 229(5284): 381–383.

    Article  Google Scholar 

  13. Moussaïd M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences, 2011, 108(17): 6884–6888.

    Article  Google Scholar 

  14. Pelechano N, Badler N I. Modeling crowd and trained leader behavior during building evacuation. IEEE Computer Graphics and Applications, 2006, 26(6): 80–86.

    Article  Google Scholar 

  15. Molnár P, Starke J. Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2001, 31(3): 433–435.

    Article  Google Scholar 

  16. McPhail C, Powers W T, Tucker C W. Simulating individual and collective action in temporary gatherings. Social Science Computer Review, 1992, 10(1): 1–28.

    Article  Google Scholar 

  17. Tucker C W, Schweingruber D, McPhail C. Simulating arcs and rings in gatherings. International Journal of Human-Computer Studies, 1999, 50(6): 581–588.

    Article  Google Scholar 

  18. Lin M C, Manocha D. Virtual cityscapes: Recent advances in crowd modeling and traffic simulation. Frontiers of Computer Science in China, 2010, 4(3): 405–416.

    Article  Google Scholar 

  19. Sewall J, Wilkie D, Merrell P et al. Continuum traffic simulation. Computer Graphics Forum, 2010, 29(2): 439–448.

    Article  Google Scholar 

  20. Lin M, Guy S, Narain R et al. Interactive modeling, simulation and control of large-scale crowds and traffic. In Proc. the 2nd International Workshop Motio in Games (MIG), Nov. 2009, pp.94–103.

  21. Lu X, Chen W, Xu M et al. AA-FVDM: An accident-avoidance full velocity difference model for animating realistic street-level traffic in rural scenes. Computer Animation and Virtual Worlds, 2014, 25(1): 83–97.

    Article  Google Scholar 

  22. Lu X, Xu M, Chen W et al. Adaptive-AR model with drivers’ prediction for traffic simulation. International Journal of Computer Games Technology, 2013, 2013: Article ID 904154.

  23. Chao Q, Shen J, Jin X. Video-based personalized traffic learning. Graphical Models, 2013, 75(6): 305–317.

    Article  Google Scholar 

  24. Shen J, Jin X. Detailed traffic animation for urban road net-works. Graphical Models, 2012, 74(5): 265–282.

    Article  Google Scholar 

  25. Wilkie D, Sewall J, Lin M C. Transforming GIS data into functional road models for large-scale traffic simulation. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(6): 890–901.

    Article  Google Scholar 

  26. Sewall J, van den Berg J P, Lin M C et al. Virtualized traffic: Reconstructing traffic flows from discrete spatiotemporal data. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(1): 26–37.

    Article  Google Scholar 

  27. Sewall J, Wilkie D, Lin M C. Interactive hybrid simulation of large-scale traffic. ACM Trans. Graph., 2011, 30(6): Article No.135.

  28. Wu J C, Popović Z. Realistic modeling of bird flight animations. ACM Transactions on Graphics (TOG), 2003, 22(3): 888–895.

    Article  Google Scholar 

  29. Klotsman M, Tal A. Animation of flocks flying in line formations. Artificial Life, 2012, 18(1): 91–105.

    Article  Google Scholar 

  30. Wang X, Jin X, Deng Z et al. Inherent noise-aware insect swarm simulation. Computer Graphics Forum, 2014. (to be appeared)

  31. Tu X, Terzopoulos D. Artificial fishes: Physics, locomotion, perception, behavior. In Proc. the 21st ACM SIGGRAPH, July 1994, pp.43–50.

  32. Anderson E F, McLoughlin L, Liarokapis F et al. Developing serious games for cultural heritage: A state-of-the-art review. Virtual Reality, 2010, 14(4): 255–275.

    Article  Google Scholar 

  33. Schreckenberg M, Sharma S D. Pedestrian and Evacuation Dynamics. Springer-Verlag, 2002.

  34. Thalmann D. Populating virtual environments with crowds. In Proc. the 2006 ACM International Conference on Virtual Reality Continuum and Its Applications, June 2006, p.11.

  35. Pelechano N, Allbeck J, Badler N I. Virtual Crowds: Methods, Simulation, and Control. Morgan and Claypool Publishers.

  36. Pettré J, Kallmann M, Lin M C. Motion planning and autonomy for virtual humans class notes. In Proc. ACM SIG-GRAPH 2008 Classes, Aug. 2008, Article No.42.

  37. Shopf J, Barczak J, Oat C et al. March of the Froblins: Simulation and rendering massive crowds of intelligent and detailed creatures on GPU. In Proc. ACM SIGGRAPH 2008 Games, Aug. 2008, pp.52–101.

  38. Huerre S, Lee J, Lin M et al. Simulating believable crowd and group behaviors. In Proc. ACM SIGGRAPH Asia 2010 Courses, Dec. 2010, Article No.13.

  39. Zhou S, Chen D, Cai W et al. Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation (TOMACS), 2010, 20(4): Article No.20.

  40. Kapadia M, Badler N I. Navigation and steering for autonomous virtual humans. Wiley Interdisciplinary Reviews: Cognitive Science, 2013, 4(3): 263–272.

    Google Scholar 

  41. Hughes R L. A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological, 2002, 36(6): 507–535.

    Article  Google Scholar 

  42. Hughes R L. The flow of human crowds. Annual Review of Fluid Mechanics, 2003, 35: 169–182.

    Article  Google Scholar 

  43. Treuille A, Cooper S, Popovic Z. Continuum crowds. In Proc. ACM SIGGRAPH 2006, July 2006, pp.1160–1168.

  44. Narain R, Golas A, Curtis S et al. Aggregate dynamics for dense crowd simulation. ACM Trans. Graph., 2009, 28(5): Article No. 122.

  45. Reynolds C W. Flocks, herds and schools: A distributed behavioral model. In Proc. the 14th ACM SIGGRAPH, July 1987, pp.25–34.

  46. Reynolds C W. Steering behaviors for autonomous characters. In Proc. Game Developers Conference, Mar. 1999.

  47. Helbing D, Molnar P. Social force model for pedestrian dynamics. Physical Review E, 1995, 51(5): 4282–4286.

    Article  Google Scholar 

  48. Cordeiro O C, Braun A, Silveria C B et al. Concurrency on social forces simulation model. In Proc. the 1st International Workshop on Crowd Simulation, Nov. 2005.

  49. Gayle R, Moss W, Lin M C et al. Multi-robot coordination using generalized social potential fields. In Proc. IEEE International Conference on Robotics and Automation, May 2009, pp.106–113.

  50. Pelechano N, Allbeck J M, Badler N I. Controlling individual agents in high-density crowd simulation. In Proc. ACM SIG-GRAPH/Eurographics Symposium on Computer Animation, Aug. 2007, pp.99–108.

  51. He¨ıgeas L, Luciani A, Thollot J et al. A physically-based particle model of emergent crowd behaviors. arXiv Preprint arXiv:1005.4405, 2010.

  52. Lakoba T I, Kaup D J, Finkelstein N M. Modifications of the Helbing-Molnar-Farkas-Vicsek social force model for pedestrian evolution. Simulation, 2005, 81(5): 339–352.

    Article  Google Scholar 

  53. Sugiyama Y, Nakayama A, Hasebe K. 2-dimensional optimal velocity models for granular flows. In Pedestrian and Evacuation Dynamics, Schreckenberg M, Sharma SD (eds.), 2001, pp.155–160.

  54. Sud A, Gayle R, Anderson E et al. Real-time navigation of independent agents using adaptive roadmaps. In Proc. the 2007 ACM Symposium on Virtual Reality Software and Technology, Oct. 2007, pp.99–106.

  55. Guy S J, Kim S, Lin M C et al. Simulating heterogeneous crowd behaviors using personality trait theory. In Proc. the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aug. 2011, pp.43–52.

  56. Durupïnar F, Pelechano N, Allbeck J M et al. How the ocean personality model affects the perception of crowds. IEEE Comput. Graph. App., 2011, 31(3): 22–31.

  57. Pelechano N, O’brien K, Badler N et al. Crowd simulation incorporating agent psychological models, roles and communication. In Proc. the 1st International Workshop on Crowd Simulation, Nov. 2005, pp.21–30.

  58. Durup¨ınar F. From audiences to mobs: Crowd simulation with psychological factors [Ph.D. Thesis]. Institute of Engineering and Science, Bilkent University, 2010.

  59. Beltaief O, Hadouaj S, Ghedira K. Multi-agent simulation model of pedestrians crowd based on psychological theories. In Proc. the 4th International Conference on Logistics (LOGISTIQUA), May 31–June 3, 2011, pp. 150–156.

  60. Kim S, Guy S J, Manocha D et al. Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In Proc. ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, March 2012, pp.55–62.

  61. Loscos C, Marchal D, Meyer A. Intuitive crowd behaviour in dense urban environments using local laws. In Proc. Theory f Practice of Computer Graphics 2003, June 2003, pp.122–129.

  62. Alexa M, Muller W. Representing animations by principal components. Computer Graphics Forum, 2000, 19(3): 411–418.

    Article  Google Scholar 

  63. Guy S J, Chhugani J, Curtis S et al. PLEdestrians: A least-effort approach to crowd simulation. In Proc. the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, July 2010, pp.119–128.

  64. Guy S J, Curtis S, Lin M C et al. Least-effort trajectories lead to emergent crowd behaviors. Physical review E, 2012, 85(1): 016110.

    Article  Google Scholar 

  65. Musse S R, Thalmann D. A model of human crowd behavior: Group inter-relationship and collision detection analysis. In Proc. Eurographics Workshop on Computer Animation and Simulation, Sept. 1997, pp.39–51.

  66. Sung M, Gleicher M, Chenney S. Scalable behaviors for crowd simulation. Computer Graphics Forum, 2004, 23(3): 519–528.

    Article  Google Scholar 

  67. Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 1998, 17(7): 760–772.

    Article  Google Scholar 

  68. Sud A, Anderson E, Curtis S et al. Real-time path planning in dynamic virtual environments using multiagent navigation graphs. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(3): 526–538.

    Article  Google Scholar 

  69. van den Berg J, Patil S, Sewall J et al. Interactive navigation of multiple agents in crowded environments. In Proc. ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D), Feb. 2008, pp.139–147.

  70. Guy S J, Chhugani J, Kim C et al. ClearPath: Highly parallel collision avoidance for multi-agent simulation. In Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA), Aug. 2009, pp.177–187.

  71. van den Berg J, Lin M, Manocha D et al. Centralized path planning for multiple robots: Optimal decoupling into sequential plans. Robotics: Science and Systems (RSS), 2009.

  72. Zheng L, Zhao J, Cheng Y et al. Geometry-constrained crowd formation animation. Computers & Graphics, 2014, 38: 268–276.

    Article  Google Scholar 

  73. Golas A, Narain R, Lin M. Hybrid long-range collision avoidance for crowd simulation. In Proc. the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, March 2013, pp.29–36.

  74. Lv L, Mao T, Liu X et al. Optimization-based group performance deducing. Computer Animation and Virtual Worlds, 2014, 25(2): 171–184.

    Article  Google Scholar 

  75. Patil S, van den Berg J, Curtis S et al. Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(2): 244–254.

    Article  Google Scholar 

  76. Pettré J, Ondrej J, Olivier A H et al. Experiment-based modeling, simulation and validation of interactions between virtual walkers. In Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aug. 2009, pp.189–198.

  77. Kim S, Guy S J, Manocha D. Velocity-based modeling of physical interactions in multi-agent simulations. In Proc. the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation, July 2013, pp.125–133.

  78. Wilkie D, Sewall J, Lin M. Flow reconstruction for data-driven traffic simulation. ACM Trans. Graph, 2013, 32(4): Article No.89.

  79. Wolinski D, Guy S J, Olivier A H et al. Parameter estimation and comparative evaluation of crowd simulations. Computer Graphics Forum, 2014, 33(2): 303–312.

    Article  Google Scholar 

  80. Kapadia M, Singh S, Hewlett W et al. Egocentric affordance fields in pedestrian steering. In Proc. the 2009 Symposium on Interactive 3D Graphics and Games, Feb. 2009, pp.215–223.

  81. Yu Q, Terzopoulos D. A decision network framework for the behavioral animation of virtual humans. In Proc. the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2007, pp.119–128.

  82. Kulpa R, Olivier A H, Ondrej J et al. Imperceptible relaxation of collision avoidance constraints in virtual crowds. ACM Trans. Graph., 2011, 30(6): Article No.138.

  83. Ondřej J, Pettre J, Olivier A H et al. A synthetic-vision based steering approach for crowd simulation. ACM Transactions on Graphics (TOG), 2010, 29(4): Article No.123.

  84. Metoyer R A, Hodgins J K. Reactive pedestrian path following from examples. The Visual Computer, 2004, 20(10): 635–649.

    Article  Google Scholar 

  85. Lee K H, Choi M G, Hong Q et al. Group behavior from video: A data-driven approach to crowd simulation. In Proc. the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Aug. 2007, pp.109–118.

  86. Lerner A, Chrysanthou Y, Lischinski D. Crowds by example. Computer Graphics Forum, 2007, 26(3): 655–664.

    Article  Google Scholar 

  87. Li Y, Christie M, Siret O et al. Cloning crowd motions. In Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation, July 2012, pp.201–210.

  88. Ju E, Choi M G, Park M et al. Morphable crowds. ACM Transactions on Graphics (TOG), 2010, 29(6): Article No.140.

  89. Flagg M, Rehg J M. Video-based crowd synthesis. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(11): 1935–1947.

    Article  Google Scholar 

  90. Sun L, Li X, Qin W. Simulating realistic crowd based on agent trajectories. Computer Animation and Virtual Worlds, 2013, 24(3/4): 165–172.

    Article  Google Scholar 

  91. Kwon T, Lee K H, Lee J et al. Group motion editing. ACM Transactions on Graphics (TOG), 2008, 27(3): Article No.80.

  92. Lerner A, Fitusi E, Chrysanthou Y et al. Fitting behaviors to pedestrian simulations. In Proc. the 2009 ACM SIG-GRAPH/Eurographics Symposium on Computer Animation, Aug. 2009, pp.199–208.

  93. Guy S J, van den Berg J, Liu W et al. A statistical similarity measure for aggregate crowd dynamics. ACM Transactions on Graphics (TOG), 2012, 31(6): Article No.190.

  94. Lai Y C, Chenney S, Fan S. Group motion graphs. In Proc. the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, July 2005, pp.281–290.

  95. Courty N, Corpetti T. Crowd motion capture. Computer Animation and Virtual Worlds, 2007, 18(4–5): 361–370.

    Article  Google Scholar 

  96. Paris S, Pettré J, Donikian S. Pedestrian reactive navigation for crowd simulation: A predictive approach. Computer Graphics Forum, 2007, 26(3): 665–674.

    Article  Google Scholar 

  97. Chenney S. Flow tiles. In Proc. the 2004 ACM SIG-GRAPH/Eurographics Symposium on Computer Animation, Aug. 2004, pp.233–242.

  98. Pelkey C D, Allbeck J M. Populating virtual semantic environments. Computer Animation and Virtual Worlds, 2014, 24(3/4): 405–412.

    Google Scholar 

  99. Kraayenbrink N, Kessing J, Tutenel T et al. Semantic crowds. Entertainment Computing, 2014. (to be appeared)

  100. Golas A, Narain R, Lin M. Hybrid long-range collision avoidance for crowd simulation. In Proc. the 2013 ACM SIG-GRAPH Symposium on Interactive 3D Graphics and Games, March 2013, pp.29–36.

  101. Kim S, Guy S J, Lin M C et al. Predicting pedestrian trajectories using velocity-space reasoning. In Proc. the 10th Workshop on the Algorithm Foundations of Robotics, June 2012, pp. 609–623.

  102. Yeh H, Curtis S, Patil S et al. Composite agents. In Proc. the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, July 2008, pp. 39–47.

  103. Lemercier S, Jelic A, Kupla R et al. Realistic following behaviors for crowd simulation. Computer Graphics Forum, 2012, 31(2): 489–498.

    Article  Google Scholar 

  104. Karamouzas I, Overmars M. Simulating and evaluating the local behavior of small pedestrian groups. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(3): 394–406.

    Article  Google Scholar 

  105. Mao T, Jiang H, Xia S et al. Recent advances on human crowd simulation. International Journal of Virtual Reality, 2010, 9(3): 27–32.

    Google Scholar 

  106. Jiang H, Xu W, Mao T et al. Continuum crowd simulation in complex environments. Computers & Graphics, 2010, 34(5): 537–544.

    Article  Google Scholar 

  107. Jiang H, Xu W, Mao T et al. Crowds flow in complex environment. In Proc. the 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, Aug. 2009, pp. 54–57.

  108. Jiang H, Xu W, Mao T et al. A semantic environment model for crowd simulation in multilayered complex environment. In Proc. the 16th ACM Symposium on Virtual Reality Software and Technology, Nov. 2009, pp.191–198.

  109. Mao T, Ye Q, Jiang H et al. Evoking panic in crowd simulation. Transactions on Edutainment VI, Pan Z, Cheok A D, Müller W (Eds.), 2011, pp.79–88.

  110. Jin X, Xu J, Wang C C L et al. Interactive control of large-crowd navigation in virtual environments using vector fields. IEEE Computer Graphics and Applications, 2008, 28(6): 37–46.

    Article  Google Scholar 

  111. Jin X, Wang C C L, Huang S et al. Interactive control of real-time crowd navigation in virtual environment. In Proc. the 2007 ACM Symposium on Virtual Reality Software and Technology, Nov. 2007, pp.109–112.

  112. Gu Q, Deng Z. Context-aware motion diversification for crowd simulation. IEEE Computer Graphics and Applications, 2011, 31(5): 54–65.

    Article  Google Scholar 

  113. Gu Q, Deng Z. Generating freestyle group formations in agent-based crowd simulations. IEEE Computer Graphics and Applications, 2013, 33(1): 20–31.

    Article  Google Scholar 

  114. Zhang M, Xu M, Han L et al. Virtual Network Marathon with immersion, scientificalness, competitiveness, adaptability and learning. Computers & Graphics, 2012, 36(3): 185–192.

    Article  MathSciNet  Google Scholar 

  115. Xu J, Jin X, Yu Y et al. Shape-constrained flock animation. Computer Animation and Virtual Worlds, 2008, 19(3/4): 319–330.

    Article  Google Scholar 

  116. Xu M, Wu Y, Ye Y. Smooth and efficient crowd transformation. In Proc. the 20th ACM International Conference on Multimedia, Oct. 2012, pp.1189–1192.

  117. Xu M, Wu Y, Ye Y et al. Collective crowd formation transform with mutual information based runtime feedback. Computer Graphics Forum, 2014. (to be appeared)

  118. Aw A, Rascle M. Resurrection of "second order" models of traffic flow. SIAM Journal on Applied Mathematics, 2000, 60(3): 916–938.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, 450000, China

    Ming-Liang Xu

  2. Beijing Key Lab of Mobile Computing and Pervasive Devices, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Hao Jiang

  3. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, China

    Xiao-Gang Jin

  4. Department of Computer Science, University of Houston, Houston, TX, 77204-3010, U.S.A.

    Zhigang Deng

Authors
  1. Ming-Liang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-Gang Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhigang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhigang Deng.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61202207, 61100086, 61272298, 61210005, 61472370, 61170214, and 61328204, the National Key Technology Research and Development Program of China under Grant Nos. 2013BAH23F01, 2013BAK03B07, and 2013BAK03B0, the Postdoctoral Science Foundation of China under Grant Nos. 2012 M520067 and 2013 T60706, the National Nonpro¯t Industry Speci¯c Program of China under Grant No. 2013467058, and the Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20124101120005.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 121 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, ML., Jiang, H., Jin, XG. et al. Crowd Simulation and Its Applications: Recent Advances. J. Comput. Sci. Technol. 29, 799–811 (2014). https://doi.org/10.1007/s11390-014-1469-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-014-1469-y

Keywords

Search

Navigation