a multidisciplinary approach to crowd studies

A multidisciplinary approach to crowd studies

Dr. Sara ManzoniComplex Systems and Artificial Intelligence research center

Department of Computer Science, Systems and Communication

University of Milano-Bicocca

4th Summer School AACIMP-2009Achievements and Applications of Contemporary Informatics,

Mathematics and Physics

Lecture 1 – 11.08.2009

What is a Crowd? Contributions from social sciences, social psychology on Human behavior and social

collectivities

Some definitions

• “Too many people in too little space” (Kruse, 1996)

• “A gathering of individuals that influence one another and share a purpose, intent or emotional state in a limited space” [Blumer]

• A crowd is a form of collective action: “two or more persons engaged in one or more actions (e.g. locomotion, orientation, vocalization, verbalization, gesticulation, and or manipulation), judged common or concerted on one or more dimensions (e.g. direction, velocity, time, or substantive content” (McPhail, 1991)

What is a Crowd? Contributions from social sciences, social psychology on Human behavior and social

collectivitiesTheories

• Contagion/Transformation Theory (Tarde; Le Bon; Blumer, Canetti)

• Convergence Theory (Berk, Floyd Allport, Neal Miller, John Dollard)

• Emergent Norm Theory (Turner, Killian)

• Value Added Theory (Smelser)

Gabriel Tarde

Gustave LeBon

Robert Ezra Park

Herbert Blumer

Elias Canetti

Neil Smelser

Traditional Theories of Crowd Behavior

Contagion Theory Crowd behaviour is irrational

The crowd • exert an effect on its members • forces individuals to action thanks to anonymity that

encourages people to abandon rationality and responsibility

• helps emotion propagation that can drive to irrational and suitably violent action


Convergence theory Crowd behavior is rational

Crowd behavior is • not the result of the crowd itself• carried inside the crowd by specific individuals

• People that would like to behave and act in a certain way come together in order to form and constitute a crowd

• Crowd behavior expresses values and beliefs that are already present in the population (i.e. racist feelings)

• The mob is a rational product of rational values


• E mergent/ norm theory Crowd behavior is not fully predictable but it is not irrational

• People in a crowd assume different roles (e.g. some participants become leaders, other lieutenants, followers, inactive bystanders or even opponents)

• Common interests can bring people together in a crowd, but different patterns (of behavior) can emerge inside the crowd itself

• Norms inside a crowd can be vague and changing in the process of aggregation (people state their own rules while participating at the crowd)

• Decision-making has a preponderant role in the behavior of the crowd although external observers may find it difficult to realize

“ the reason why good data on crowd and collective behavior are so scarce is that data are function of theorethical guidance and existing theories provide no guidance; but, useful theories cannot be built in the absence of empirical data ...”

Freely taken from:Clark McPhail, Blumer’s theory of collective behavior: The development of a Non-symbolic interaction explanation, The Sociological Quaterly, Volume 30, Number 3, JAI press 1989

Herbert Blumer 1900-1987

Crowd study: Contributions/Open issues from computer

scienceBetter comprehension of crowd phenomena (crowd study) and development of tools (for crowd study and crowd management)– Data acquisition techniques and technologies

• Direct observation (Stalking, Questionnaires)• Scene analysis • Proximity detection (RF-ID)• Localization systems (GPS, sensor networks)

– Crowd modeling and simulation• Modeling, computational, analysis tools• Simulation and visualization tools

– Knowledge representation• Data representation and analysis • Experts’ knowledge• Available theory/results from social sciences

M ultidisciplinary F ield of

Study

Data acquisition techniques and technologies

Crowd modeling and simulation

Data Acquisition

HOW CAN CROWDS AND INDIVIDUALS BE MEASURED? WHAT CAN BE MEASURED?HOW CAN AVAILABLE TECHNOLOGIES MEASURE CROWDS?

S. Bandini, M.L. Federici, S. Manzoni“A qualitative evaluation of Technologies and Techniques for Data Acquisition on Pedestrians and Crowded”Proc. of Special session “At man’s step”@SCSC07, San Diego, CA

What are we interested in Measuring: Data on the crowd

• Number of people (static or inside a march)

• Density of the crowd• Flow, Pressure and

times of ingress/egress from a place

• Groups movement inside a crowd

• …

What are we interested in Measuring: Data on individuals

• Trajectories in a specific environment

• Walking speed in different situations

• Physical Behavior:– Queuing– Streaming– Group formation– Separation– Cohesion– Imitation– …

Measuring Crowds: HOW?Mature and emergent technologies for data

acquisition

• Direct Observation, interviews, questionnaires, stalking

• Technologies for people positioning and counting– Scene analysis: TV Camera – Global Positioning System (GPS)– Proximity technologies (Radio

Frequency IDentification – RFID)– Sensor Networks– PDAs, SmartPhones (GPRS, Wi-

Fi)– Dead reckoning (portable inertial

platform)

Stalking (following people without being seen!)

Data acquisition: an example (2005)

Application of GIS/GPS to track pedestrian movements– Position, velocity,

trajectories – Critical areas

identification

N. Koshaak (Makkah - Saudi Arabia)

0

1

2

3

4

5Scalability

Single Individual Monitoring

Entire Crowd/groups Monitoring

Indoor

Large Scale

Small Scale

Cheap

Outdoor

Precise Localization Data

Continuous Localization

Absolute Position System (GPS) Proximity Tech. (Passive RFiD) Scene Analysis (Video Analysis)

0: null quality1: insufficient2: just sufficient3: discrete4: good5: best Available

Comparing Data Acquisition Technologies and Techniques (1)

0

1

2

3

4

5Scalability

Single Individual Monitoring

Entire Crowd/groups Monitoring

Indoor

Large Scale

Small Scale

Cheap

Outdoor

Precise Localization Data

Continuous Localization

Dead Reckoning (Portable Inertial Platform) Sensor Network (ZigBee) Direct Observation (People Counting)

0: null quality1: insufficient2: just sufficient3: discrete4: good5: best Available

Comparing Data Acquisition Technologies and Techniques (2)

San Diego (CA)At man’s Step special track at Summer Computer Simulation Conference 2007Jul, 13-14 2007

S. Bandini, M.L. Federici, S. Manzoni, “A qualitative comparison of technologies for Data Acquisition on Pedestrians and Crowded Situations”

Crowd modeling and simulation HOW DO PEOPLE BEHAVE IN CROWDED SPACES AND

SITUATIONS?

SIMULATIONS ARE EXPERIMENTAL LABORATORIES FOR HUMAN SCIENCES

Crowd modeling and application directions

• Support the study of pedestrians/crowds behavior

– Envisioning of different behavioral models in realistic environments

– Possibility to perform ‘in-machina’ experiments

• Decision makers might not be experts neither on crowd dynamics nor on software and math tools

– Need of effective ways to edit, execute, visualize and analyze simulations (what-if scenarios)

• Indoor (Buildings, Shopping Centers, Stadiums) vs Outdoor (Urban spaces for public events/transport, Parades, Marches, Fairs, Sport Events, Concerts)

Examples of Crowd Dynamics

• Evacuation dynamics – normal vs panic– open/structured spaces

• Lane formation and other self-organization phenomena

• Crowd formation/dispersion• Crowd movement and behavior (e.g. in

shopping center)

TSTarget System

SWSoftware

MASComp.Model

MAbstractModel

1) Phase 1: Observation

1

22) Phase 2: Model / Theory Construction

3 3) Phase 3: Computational modeling

44) Phase 4: Software implementation

Reality0

1

2

3

4

AbstractionLevels

Schema of the abstract levels involved in a simulation

If we look at the different passages implied in the construction of a simulation model using a Mas we can see that implicitly we are working with many models. Each model represents a level of abstraction. How many levels of abstraction are involved in a simulation process?

The target system is the object of study (Physical model). It is a specific point of view on a portion of reality that we consider “isolated” from the context. The target system is determined by our observation perspective on reality, and by the aspects of reality that we want to capture (i.e. atom level; molecular; macro-level)

The abstract model of the target system can be expressed in natural language, mathematical formulas etc. but usually it is not computational. It can be, at first, also an intuitive set of rules.

The computational model is what we use to represent the abstract model. The computational model is always a formal model. It can be a model Agent Based or Cellular Automata based etc.

The Software Model (operational model) of the Computational Model

Phase 1: observation of the Target System The Target System has to be observed and data on it collected (this phase can’t be separated from the next). To observe something a selective attention and an Hypothesis on what is observed must be already present in the observer)

Phase 2: Modelization Formulation of an Hypothesis (Model), that could even be simply intuitive, vague or informal. Phase 3: Computational Modeling Translation of the Abstract Model into the Computational Model (language/entities). This Phase constitutes the point of “no return” to reality (an interpretation key that explains how translate back entities of the computational model in entities of reality is needed)

Phase 4: Software Implementation Translation of the Computational Model in the Software Code

Phases from 1 to 4 can be seen as phases of the simulation Building and are concerned with models and abstraction.

The next phases concern instead the Decoding Levels of the simulation

TS

M

MAS

SW

3

2

1) Observation

4

Target System

Software

Comp.Model

AbstractModel

1

2) Model / Theory Construction 3) Computational modeling4) Software implementation

7

7) Verification of Sim in respect to the Theory

9

9) Prediction

8

8) Validation of Theory in respect to Real Data

Reality

5) Computation (sim running)

56

6) Visualization

0

1

2

3

4

AbstractionLevels

Decoding Levels

O Output

SD SimulationDisplaying

T Theory

R Real Data

Phase 5: Software run (calculus-computation)Output: simple results of the softwareexecution Phase 6: Visualization an appropriate

translation of simulation outputs in observable objects

Simulation Displaying: the envisioning of the outputs that is often the only way to operate with the simulation dynamic data

Phase 7: Verification Simulation is running and it has to be verified in respect to our initial theory

Theory: assumptions and rules that constitute the description of the domain

Phase 8: Validation The theory has then to be checked in relation to reality on the base of indicators chosen in precedent phase

Real Data: collection of meaningful measurements in the target system

Phase 9: prediction If the theory is Validated for available data then it is possible to make predictions for future states of the Target System

1

2

3

4

TS

M

MAS

SW O

SD

T

RTarget System

Software

Comp.Model

AbstractModel

Computational Model ChangeAbstract Model Revision

Software Implementation

Check

Decoding and correcting

• Software Implementation Check: If no suitable results are obtained a deeper check into the software may be needed in order to verify that it translates properly the computational model

• Computational Model Change: After a Campaign of Simulation that gave negative results a check of the assumptions that I made to translate my theory in the model is needed. A change of the computational Model can eventually be necessary.

• Abstract Model Revision: If the Computational Model is judged adequate to capture the theory assumptions, but real data don’t give a positive feedback, the abstract model must undergo a revision that will lie on the construction on new hypothesis and empirical observations

1

2

3

4

TS

M

MAS

SW O

SD

T

RTarget System

Software

Comp.Model

AbstractModel

Analysis

Design

Inference

Interpretation

Mapping of the phases of Design, Inference, Interpretation and Analysis in the Schema

Steps in Physical Scientific Practice

From: Modeling Games in the Newtonian World, by David Hestenes

Observation

PhysicalModel

MathematicalModel

Prediction

Hypothesis/Theory Building

Translation inMathematicalFramework

Inference/Deduction

Experiment/Validation

Target System1

2

3

Software4

TS

M

MAS

SW

reality

Comp.Model

AbstractModel

TS

M

Mat

Abstraction in Mabs Abstractions in Physical Theories

Comparison Between Abstraction Implied in Physic Scientific Practice and MAS Simulation Practice

One Step More of Abstraction

is implied

Mas Model Corresponds to

Mathematical Model

Pedestrian Movement at the Micro-Scale: Social Force Model [Batty, Helbing (2001)]

• Four principles “guide” movement– Agents avoid obstacles present in the environment– Agents consider repulsive the presence of other pedestrians when

space is congestioned– Agents also attract each other (principle of the flocking)– Agents “desire” to follow a direction

• To each of these components it is associated a force that pushes the agent towards a specific direction

DesiredPosition

GeometricRepulsion

SocialRepulsion

SocialAttraction+ + + + += εNew Position Old

Position

Crowd modeling Analytical (physical) approach

• Pedestrians particles subject to forces

• Goals: forces of attraction generated by points/reference point in the space

• Interaction among pedestrians: forces generated by particles

• Social forces– Repulsive tendency to stay

at a distance– Attractive imitative

mechanisms

Lane formation

‘Freezing by heating’

D. Helbing, I. J. Farkas, T. Vicsek: Freezing by Heating in a Driven Mesoscopic System, PHYSICAL REVIEW LETTERS, VOLUME 84, NUMBER 6, 2000

Crowd modelling: Cellular Automata

• Environment bidimensional lattice of cells

• Pedestrian specific state of a cell (e.g. occupied, empty)

• Movement generated thanks to the transition rule

– an occupied cell becomes empty and an adjacent one, which was previously vacant, becomes occupied

• Choice of destination cell in a transition generally includes information on– Benefit-Cost/Gradient: information

about “cell desirability”– Magnetic Force: models the effect of

presence of other agents in the environment (attraction/repulsion of crowds)

Crowd modelling: From CA to Situated MAS

• Individuals are separated from the environment– Agents, not just cell occupancy states– may have different behaviors: several

action deliberation models can be integrated

– heterogeneous system

• Agents interact by means of mechanisms not necessarily related to underlying cell’s adjacency– Action at a distance is allowed

Situated MAS action and interaction

• Agents are situated– they perceive their context and

situation– their behaviour is based on their

local point of view– their possibility to interact is

influenced by the environment• Situated Agents Interaction models

– Often inspired by biological systems (e.g. pheromones, computational fields)

– Generally provide a modification of the environment, which can be perceived by other entities

– May also provide a direct communication (as for CAs interaction among neighbouring cells)

Situated Cellular Agents (SCA)

Multi Agent model providing• Explicit representation of

agents’ environment• Interaction model strongly

related to agents’ positions in the environment– Among adjacent agents

(reaction)– Among distant agents,

through field emission-diffusion-perception mechanism

• Possibility to model heterogeneous agents, with different perceptive capabilities and behaviour

CompareT(f×c,t) = true

emit(f)

react(s,ab,s’) react(s,ac,s’)

Situated Cellular Agents (SCA)• Formal and computational framework to

represent and study of dynamics in pedestrian systems

– autonomous interacting entities – situated in an environment whose spatial

structure represents a key factor in their behaviors (i.e. actions and interactions)

• Based on MMASS (Multilayered Multi-Agent Situated Systems)[S. Bandini, S. Manzoni, C. Simone, Dealing with Space in Multi-Agent System: a model for Situated MAS, in Proc. of AAMAS 2002, ACM Press, New York, 2002]

• MMASS relaxes constraints on uniformity, locality and closure of CA[S. Bandini, S. Manzoni, C. Simone: Enhancing Cellular Spaces by Multilayered Multi Agent Situated Systems, Proc. of ACRI 2002: 156-167]

– Open systems can be modeled– Not homogeneous agent environment – Heterogeneous agents– Interaction involving spatially not adjacent agents

SCA model

Agents andbehaviours At-a-distance

interaction

Spatialstructure

SCA Space

• Space: set P of sites arranged in a network

• Each site p∈P is defined by <ap, Fp, Pp> where– ap∈A ∪{⊥}: agent situated in p

– Fp⊂F: set of fields active in p

– Pp⊂P: set of sites adjacent to p

SCA Fields

• <Wf, Diffusionf, Comparef, Composef>

– Wf: set of field values

– Diffusionf: P X Wf X P Wf X…X Wf

field diffusion function

– Composef: Wf X …X Wf Wf

field composition function

– Comparef: Wf X Wf {True, False}

field comparison function• Fields are generated by agents to interact at-a-distance and

asynchronously

SCA Agents

• a∈A : <s,p,T>• T < ∑T, PerceptionT, ActionT>

– ∑T: set of states that agents can assume

– ActionT: set of allowed actions for agents of type T

– PerceptionT:

∑T [N X Wf1] … [N X Wf|F|]

• PerceptionT(s) = (cT(s), tT(s))

• cT(s): perception modulation

• tT(s): sensibility threshold

• An agent a = <s,p,T> perceives the field value wfi

of a field fi = <Wf, Diffusionf, >i, *i>when ciT(s)*fi

wfi,>fi tiT(s)) and

SCA Agent Actions

• ActionsT: set of actions that agents of type T can perform

• Agent behavior: perception-deliberation-action cycle– Perception of local environment (e.g. free sites, fields)– Action selection based on agent state, position and type– Action execution

• Four basic actions– intra-agent actions: triggerT(), transportT()

– inter-agent actions: emitT(), reactionT()

action: trigger(s,fi,s’)condit: state(s), perceive(fi)effect: state(s’)action: transport(p,fi,q)condit: position(p), empty(q), near(p,q), perceive(fi)effect: position(q), empty(p)

action: emit(s,f,p)condit: state(s)effect: present(f, p)

action: reaction(s, ap1, ap2, …, apn,s’)condit: state(s), agreed(ap1, ap2,…, apn)effect: state(s’)

Situated MAS and crowd modeling

• Pedestrians agents

• Environment graph, as an abstraction of the actual environmental structure

• Movement generated thanks to perception-action mechanism– Sources of signals: relevant objects

(gateways, reference points), but also other agents

– Agents are sensitive to these signals and can be attracted/repelled by them (amplification/contrast)

– Possible superposition of different such effects

transport(p,q)

SCA Crowd Modelling Approach

Definition of the MMASSspatial structure

Definition of activeelements of the

environmentand field types

Definition of mobile agents(types, states, perceptive

capabilities and behaviouralspecification)

Definition of monitoredparameters and

specificationof monitoring mechanisms

Specific simulationconfiguration (number, type,position and initial state of

mobile agents, otherparameters)

Abstract scenariospecification

Computational model for the scenario

Experiment-specificparameters

Underground scenario• An underground station (several interesting crowd

behaviors can be studied)

• Passengers' behaviors are difficult to predict: crowd dynamics emerges from single interactions

– between passengers– between single passengers and parts of the

environment (signals, constraints)

• Passengers (actions)– on board may

• have to get off • be looking for a seat or try standing beside a handle• be seated

– on the station platform may • try to reach for the exit door• get on the train

• Passengers have to match their goals with– Environment obstacles – other passengers goals– implicit behavioral rules that govern the social

interaction in underground stations

SCA model of Underground Station Scenario Spatial structure of the environment

Spatial structure discrete abstraction ofsimulation environment

SCA model of Underground Station Scenario Active Elements of the Environment and Field Types

SCA model of Underground Station Scenario Agent types

An agent type t is a triple <∑t , Perceptiont, Actiont> where

• ∑t : set of agent states• Perceptiont : specifies for every agent

state and field type– a sensitivity coefficient c modulating

(amplifies/attenuates) field values– a sensibility threshold t filtering out

fields that are considered too faint– An agent perceives a field fT when

CompareT(f*c,t) = true• Actiont : behavioral specification for

agents of type t

Passengers

Seat

Station Exit

Wagon Exit

Handles

Dynamic Agents

Static Agents (active objects)

Agent

Types

SCA model of Underground Station Scenario Passengers behavior as state-transition diagram and

attitudes towards movement

Example based on designed behaviors for the case study.Should be calibrated SCA platform editor of agents’ behaviors

W

G

P

E

Waiting: passengers on the platformwaiting for a train

Get Off: people on the wagon that have toget off the train

Passenger: agent on the train that has noimmediate necessity to get off

Exit: passenger that has got down the trainand goes away from the station

S Seated: agent seated on a seatof the wagon

State Transition

W

G

E

P

S

SCA model of Underground Station Scenario Mobile Agents – Movement

State E xits D oors Seats H andles P resence E xit press.

W - Attract (2) - - Repel (3) Repel (1)

P - - Attract (1) Attract (2) Repel (3) Repel (2)

G - Attract (1) - - Repel (2) -

S - Attract (1) - - - -

E Attract (1) - - - Repel (2) -

When multiple signals are perceived, agents evaluate next-destination site according to weighted sum of perceived values

transport(p,q)

∑

SCA model of Underground Station Scenario (demo)

1) Pedestrian agent in site P (on the wagon)

Field “Handle”



1) Available seats (only one) emit a field

that is perceived as attractive by the agent

Emit (s, f, p)Field “Handle”

Field “Seat”



2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold

3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense Trasport (p,q)

Emit (s, f, p)




3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense

1) Process iterated until the agent reaches the site where the local max of intensity is perceived

Trasport (q,t)

Emit (s, f, p)


Trasport (t,r)





Emit (s, f, p)


Trasport (r,g)

Emit (s, f, p)






Trasport (g,k)





Emit (s, f, p)


React (p,sg,s)

React (a,s s,o)1) Pedestrian agent in site P (on the

wagon)2) Available seats (only one in site Q)

emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold



3) Available_seat agent and pedestrian agent change their state simultaneously (agent seat turns into the occupied state and stops emitting fields while passenger turns into state seated)

Underground Case Study: model execution

• Simulation configuration– 6 agents getting off– 8 agents getting on

Visualization of system dynamics 3D rendering of 2D simulations (offline animation)

• Java based bidimensional simulator • Exported log of the simulation including

– Definition of the spatial structure– System dynamics

• MaxScript that allows 3D Studio Max to generate an animation representing the simulated scenario

Avatar#001#001#001#004#003#000@Avatar#002#002#001#003#005#000@Avatar#001#002#010#003#002#000@Avatar#002#001#001#003#004#000@Space#001#001#001#001#006#000@Space#001#002#001#002#005#000@Space#001#003#001#004#005#000@Space#001#004#001#004#004#000@Space#001#005#001#003#004#000@Space#001#006#001#003#002#000@Space#001#007#001#004#002#000@Space#001#008#001#005#002#000@

Why 3D? • To obtain an effective visualization of simulation dynamics• To obtain a machine-readable spatial abstraction in a semi-

automatic way from existing models of the environment• To exploit the rich information of a 3D model to implement highly

realistic perception

Freezing by Heating - Experiments

Helbing

• 3 densities (20-40-60%)• 10 simulations for each densities• After few turns pedestrians-agents get stacked in

front of the exit (80-90% of pedestrian population can’t move for the turn)

Schadschneider

Lane Formation: Experiments

• Simple Behavioural Model: pedestrians are attracted by the desired exit

(Without collision avoidance the phenomenon of freezing by heating is detected also at low densities)

• Introduction of repulsion:

– Each pedestrian at the beginning of the turn emits a presence field that is spread in adjacent sites

– Each pedestrian evaluates negatively the sites where it is perceived the presence of other pedestrians (presence of pedestrians with opposite direction is evaluated more negatively)

• Lane Formation only at low densities

Lane Formation: Experiments• Introduction in the model of the

possibility of an exchange of position between pedestrians that want to occupy one the site of the other

• Introduction of a concept of “irritation” that leads pedestrians to:

– Search for new paths (empty sites become more desirable after some turns of immobility)

– Attempt to exchange position with other agents (less sensitivity to presence field of other pedestrians)

• Experimentations performed with density from 10 to 90%:

4 different configurations of parameters (2 experiments for each density, 500 turns each)

3 different corridor geometries

0

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0,6

0,8

1

1,2

10 20 30 40 50 60 70 80 90 95 100

Pedestrian Density (Pedestrians / Total Sites)

Pede

stria

n Sp

eed

(Site

s Pe

r Tur

n)

Togawa

Simulation 1

Simulation 2

Simulation 3

Simulation 4

Blue&Adler

Evacuation scenarios

Singola uscita D ue uscite

I Thank You

…… continues ……

a multidisciplinary approach to crowd studies

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