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
Group Composition
Mathematicians
Computer scientists
Criminologists
Crowd management
practitioner
Group Process
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
Modeling Project
• Social dynamics
• Macro factors – Fuzzy Cognitive Map (FCM)
• Micro factors – Cellular Automata (CA)
• Threshold analysis: Major crowd disturbance
Crowd Psychology
A people behaviour: Disruptive
B people behaviour: Observers  Participants
C people behaviour: Guardians
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
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
Creating the FCM
Enter here:
c1
to
(affected)
c2
c3
c4
c5
c6
P(incident)
Please Enter:
c1
1 Very Low
c2
2 Low
from
c3
3 Moderate
(affecting)
c4
4 High
c5
5 Very High
c6
Increases
Words:
c1
(affecting)
to
(affected)
c2
c3
Decreases
c4
c5
c6
P(incident)
c1
c1
Cohesive Social Control Mechanisms
c2
c2
Structured Environmental Factors
c3
c3
Unfavourable Situational Factors
c4
c4
Technological Connectivity
c5
c5
Volatile Demographics
c6
c6
Risk of Event
Colour
c1
c1
to
(affected)
c2
c3
0
c4
c5
c6
0
c2
0
0
from
c3
0
(affecting)
c4
0
0
c5
0
0
c6
0
0
0
P(incident)
0
0
0
0
0
0
0
0
0
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0
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0
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0
FCM – CA relationship
Micro Interactions – CA model
• Each cell has a stable character
• A type person
• B type person
C (+ 1)
• C type person
A (-0.8)
C (+0.5)
B (-0.1)
• Each cell has a disruptive risk
• -1 ↔ disruptive
• 0 ↔ observing - susceptible
• 1 ↔ active guardianship
A (-0.5)
Disruptive to Guarding
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
Group Process
CA transition rules
Deteriorating
Boredom
A, B  Disruptive:-rn2
exponential negative
B  Observing:-rsp(s)
linear inward
Preventing
Respecting
A, B  Guarding,
C  Disruptive: rn2
exponential positive
A, C  Inactive,
C  Guarding: 0
no interaction
Results – Unfavourable FCM
Results – Unfavourable FCM
Results – Favourable FCM
Results – Favourable FCM
Results – More A Types
Results – More A Types
Results – Fewer A Types
Results – Fewer A Types
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
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
Vahid Dabbaghian, SFU vdabbagh@sfu.ca
Vijay Mago, SFU vmago@sfu.ca
Crowd Dynamics:
Simulating Major Crowd Disturbances
QUESTIONS?