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Crowd Dynamics: Simulating Major Crowd Disturbances

Crowd Dynamics: Simulating Major Crowd Disturbances. Valerie Spicer, PhD and Hilary Kim Morden , PhD Student Modelling of Complex Social Systems - MoCCSy. CCJA-ACJA October 2013.

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Crowd Dynamics: Simulating Major Crowd Disturbances

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  1. Crowd Dynamics: Simulating Major Crowd Disturbances Valerie Spicer, PhD and Hilary Kim Morden, PhD Student Modelling of Complex Social Systems - MoCCSy CCJA-ACJA October 2013 This is a joint work with Piper Jackson, PhD, Andrew Reid, PhD Student, Vijay Mago, PhD and Vahid, PhD

  2. Group Composition Mathematicians Criminologists Computer scientists Crowd management practitioner

  3. Group Process

  4. Literature review • LeBon (1960)Group mind / psychological crowds • Zimbardo(2007) De-individuation theory • McPhail(1991) Crowd crystals • Stott, Hutchison, & Drury (2001) Hooligans/ESIM • Forsyth (2006)6 factors of collective behaviour • McHugh (2010) Emotions of body movement

  5. Modeling Project • Social dynamics • Macro factors – Fuzzy Cognitive Map (FCM) • Micro factors – Cellular Automata (CA) • Threshold analysis: Major crowd disturbance

  6. Crowd Psychology A people behaviour: Disruptive B people behaviour: Observers  Participants C people behaviour: Guardians

  7. Macro Factors • Effective social control mechanisms • Police – city – transit • Structured environmental factors • Road design – event location • Unfavourable situational factors • Suitable target – podiums in the environment • Unstructured technological connectivity • Text messaging – Twitter – Facebook • Volatile demographics • Younger people – intoxication – gender distribution • High risk event • Divisive event – non-family oriented

  8. Creating the Fuzzy Cognitive Map • Group process – used surveys • Requiring further definition of factors • Started with 26 factors reduced to 6 factors • Verified definitions and strengths with independent group member

  9. Creating the FCM

  10. FCM – CA relationship

  11. Micro Interactions – CA model • Each cell has a stable character • A type person • B type person • C type person • Each cell has a disruptive risk • -1 ↔disruptive • 0 ↔ observing - susceptible • 1 ↔ active guardianship A (-0.8) C (+ 1) C (+0.5) A (-0.5) B (-0.1)

  12. Disruptive to Guarding

  13. Fuzzy Transitions • 9 rules: one for each combination: • {A, B, C}  {Disrupting,Observing,Guarding} • All rules applied fuzzily each iteration • Takagi-Sugeno-Kang: Each rule is a mathematical function, e.g., f(x, y) = y - x

  14. Group Process

  15. CA transition rules Deteriorating A, B  Disruptive:-rn2 exponential negative Preventing A, B  Guarding, C  Disruptive: rn2 exponential positive Boredom B  Observing:-rsp(s) linear inward Respecting A, C  Inactive, C  Guarding: 0 no interaction

  16. Results – Unfavourable FCM

  17. Results – Unfavourable FCM

  18. Results – Favourable FCM

  19. Results – Favourable FCM

  20. Results – More A Types

  21. Results – More A Types

  22. Results – Fewer A Types

  23. Results – Fewer A Types

  24. Future Directions • Model Adjustments to enhance precision • FCM expansion – factor interaction • CA modification – non-adjacent cell influences • Data testing and further validation of model • Verification with crowd control experts

  25. Crowd Dynamics: Simulating Major Crowd Disturbances Valerie Spicer, SFU vspcicer@sfu.ca Hilary Kim Morden, SFU hmorden@sfu.ca Lee Patterson, VPD lee.patterson@vpd.ca Andrew Reid, SFU aar@sfu.ca Piper Jackson, SFU pjj@sfu.ca VahidDabbaghian, SFU vdabbagh@sfu.ca Vijay Mago, SFU vmago@sfu.ca

  26. Crowd Dynamics: Simulating Major Crowd Disturbances QUESTIONS?

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