Crowd Simulation

1. Crowd Simulation Ilknur Kaynar – Kabul COMP 259 – Spring 2006

2. Overview • Motivation • Simulating dynamic features of escape panic D. Helbing, I. Farkas, and T. Vicsek • Hierarchical Model for Real Time Simulation of Virtual Human Crowds Soraia Raupp Musse, Daniel Thalmann • Constrained Animation of Flocks Matt Anderson, Eric McDaniel and Stephen Chenney • Scalable Behaviors for Crowd Simulation Mankyu Sung, Michael Gleicher and Stephen Chenney • Summary

3. Motivation • Real worlds: crowds are ubiquitous • Non-real time applications: (films, cut-scenes of games) crowds used more and more, usually to increase epic dimensions • Real-time applications: (games, training simulations) crowds are still rare, most interactive worlds are “ghost towns”

4. Applications • Entertainment industry (animation production, computer games) • Training of police & military (demonstrations, riots handling) • Architecture (planning of buildings, towns, visualization) • Safety science (evacuation of buildings, ships, airplanes) • Sociology (crowd behavior) • Physics (crowd dynamics)

5. Common approaches Particle systems Agent based models Cellular automata Probability networks Social-force networks Exotic approaches Fractals Chaos model Flow and network models Perceptual control theory Approaches

6. State of the Art (Movies)

7. Simulating dynamic features of escape panic Dirk Helbing, Illes Farkas, and Tamas Vicsek Nature, 2000

8. Contribution • Proposes a model of pedestrian behaviour to investigate the mechanisms of panic and jamming by uncoordinated motion in crowds

9. Characteristic features of escape panic • People move or try to move considerable faster than normal • Individuals start to pushing, and interactions become physical • Moving becomes uncoordinated • At exist, arching and clogging are observed • Jams build up • Pressure on walls and steel barriers increase • Escape is further slowed by fallen or injured people acting as ‘obstacles’

10. The Problem & Solution Crowd stampedes can be deadly People act in uncoordinated and dangerous ways when panicking It is difficult to obtain real data on crowd panics Model people as self-driven particles Model physical and socio-psychological influences on people’s movement as forces Simulate crowd panics and see what happens

11. Acceleration of Simulated People • vi0(t) = desired speed • ei0(t) = desired direction • vi(t) = actual velocity • τi = characteristic time • mi = mass

12. Forces from Other People • Force from other people’s bodies being in the way • Force of friction preventing people from sliding • Psychological “force” of tendency to avoid each other • Sum of forces of person j on person i is fij

13. Total Force of Other People sum of the people’s radii distance between people`s centers of mass normalized vector from j to i • Aiexp[(rij – dij)/Bi]nij is psychological “force” • Ai and Bi are constants psychological force

14. Physical Forces • g(x) is 0 if the people don’t touch and x if they do touch • k and κ are constants tangential velocity difference tangential direction force from other bodies force of sliding friction

15. Forces from Walls • Forces from walls are calculated in basically the same way as forces from other people

16. Values Used for Constants and Parameters • Insufficient data on actual panic situations to analyze the algorithm quantitatively • Values chosen to match flows of people through an opening under non-panic conditions

17. Simulation of Clogging

18. Simulation of Clogging • As desired speed increases beyond 1.5m s-1, it takes more time for people to leave • As desired speed increases, the outflow of people becomes irregular • Arch shaped clogging occurs around the doorway

19. Widening Can Create Crowding • The danger can be minimized by avoiding bottlenecks in the construction of buildings • However, that jamming can also occur at widenings of escape routes

20. Mass Behavior • Panicking people tend to exhibit either herding behavior or individual behavior, or try to mixture of both • Herding simulated using “panic parameter” pi Individual direction Average direction of neighbors

21. Effects of Herding

22. Effects of Herding • Neither individuals nor herding behaviors performs well • Pure individualistic behavior: each pedestrian finds an exit only accidentally • Pure herding behavior: entire crowd will eventually move into the same and probably blocked direction

23. Injured People Block Exit

24. A Column Can Increase Outflow

25. Conclusion • Bottlenecks cause clogging • Asymmetrically placed columns around exits can reduce clogging and prevent build up of fatal pressures • A mixture of herding and individual behavior is ideal

26. Demos http://angel.elte.hu/panic/

27. Future work • Are parameters based on non-panic situations correct for panic situations? • How can we get quantitative data about panic situations to test simulations? • What happens when injured people are allowed to fall over (and possibly be trampled)?

28. Hierarchical Model for Real Time Simulation of Virtual Human Crowds Soraia Raupp Musse, Daniel Thalmann IEEE Transactions on Visualization and Computer Graphics (2001)

29. Overview • Proposes a model to automatically generate human crowds based on groups, instead of individuals • Presents three different ways of controlling crowd behaviors

30. Contributions • Multilevel hierarchy formed by crowd, groups and agents • Various degrees of autonomy • Scripted behaviors (programmed behavior) • Interactive control (guided behavior) • Rule based behaviors (reactive behaviors) • Groups-based behaviors, where agents are simple structures and groups are more complex structure

31. Terms • Entities • Virtual human agent: a humanoid whose behaviors are inspired by those of humans • Group: Groups of agents • Crowd: Set of groups • Intentions: goals of the entities • Knowledge: information of the virtual environment • Belief: internal status of entities • Events: incidence of something causing a specific reaction

32. ViCrowd • A system address two main issues: • Crowd behavior • Crowd structure • Based on flocking systems • Includes a simple definition of behavioral rules using conditional events and reactions

33. Control of behaviors

34. Crowd Structure

35. Crowd Information

36. Knowledge • Crowd obstacles • All the objects and the areas that the crowd can walk • Crowd motion and action • Described using goals • Interest points (IP) : crowd should pass • Action points (AP) : crowd can go and perform an action • IP and AP define the crowd paths • Between two goals, different Bezier curves are created for each individual • Group knowledge • Processed by the leader of the group • Contain location of other groups and their knowledge, belief and intentions

37. Beliefs • Crowd and Groups Behaviors • Flocking • Group ability to walk together in a structured group movement • Following • Group ability to follow a group or an individual motion • Goal Changing • In sociological effects, agents can change their groups and become a leader

38. Beliefs • Crowd and Groups Behaviors • Attraction • Groups of agents are attracted around an attraction point

39. Beliefs • Crowd and Groups Behaviors • Repulsion • Group ability to be repulsed from a specific location or region • Split • Subdivision of a group to generate one or more groups

40. Beliefs • Crowd and Groups Behaviors • Space Adaptability • Group ability to occupy all the walking space • Safe-Wandering • Evaluate and avoid collision contacts with agents and objects

41. Beliefs • Emotional Status • Sad, calm, happy, regular, etc • Way of walking, walking speed and range of basic actions • Individual Beliefs • In sociological effects, individuals has goal changing behavior and domination value

42. Intentions • Crowd knowledge is used to generate crowd intentions • Based on crowd intentions, groups intentions are generated in a random way

43. Inter-dependence between the levels of information

44. Overview of Model

45. Results & Demos

46. Results & Demos SB : Scripted behavior GB: Guided behavior RB: Reactive behavior

47. Summary • Simulations are generated with various levels of realism including scripted, reactive and guided behavior • Crowd is modeled using hierarchical structure which is based on groups, not individuals

48. Constrained Animation of Flocks Matt Anderson, Eric McDaniel and Stephen Chenney Eurographics/SIGGRAPH Symposium on Computer Animation 2003

49. Motivation • In real applications, the animator usually wants to specify what happens in the scene!

50. Contribution • A method for imposing hard constraints on the paths of agents at specific times while retaining the global characteristics of an unconstrained flock