crowd dynamics: simulating major crowd disturbances

Crowd Dynamics: SimulatingMajor Crowd Disturbances

This is a joint work with Piper Jackson, PhD, Andrew Reid, PhD Student, Vijay Mago, PhD and Vahid , PhD

Valerie Spicer, PhD and Hilary Kim Morden, PhD StudentModelling of Complex Social Systems - MoCCSy

CCJA-ACJA October 2013

Mathematicians

Criminologists

Computer scientists

Crowd management practitioner

Group Composition

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 FCMEnter here: to (affected)

c1 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: to (affected) Decreases

c1 c2 c3 c4 c5 c6 P(incident)

c1 c1 Cohesive Social Control Mechanisms

c2 c2 Structured Environmental Factors

c3 c3 Unfavourable Situational Factors

(affecting) c4 c4 Technological Connectivity

c5 c5 Volatile Demographics

c6 c6 Risk of Event

Colour to (affected)

c1 c2 c3 c4 c5 c6 P(incident)

c1 0 0 0 0

c2 0 0 0 0 0

from c3 0 0 0 0 0

(affecting) c4 0 0 0 0 0

c5 0 0 0 0

c6 0 0 0 0

FCM – CA relationship

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)

A (-0.5)

B (-0.1)

C (+ 1) C (+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

DeterioratingA, B Disruptive:-rn2

exponential negative

PreventingA, B Guarding,C Disruptive: rn2

exponential positive

BoredomB Observing:-rsp(s) linear inward

RespectingA, C Inactive,C Guarding: 0no 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.caLee Patterson, VPD lee.patterson@vpd.ca

Andrew Reid, SFU aar@sfu.ca Piper Jackson, SFU pjj@sfu.ca

Vahid Dabbaghian, SFU vdabbagh@sfu.caVijay Mago, SFU vmago@sfu.ca

Crowd Dynamics: Simulating Major Crowd Disturbances

QUESTIONS?