=Paper=
{{Paper
|id=Vol-460/paper-2
|storemode=property
|title=A Fuzzy Ontology for the Classification of Crowds at Concerts
|pdfUrl=https://ceur-ws.org/Vol-460/paper02.pdf
|volume=Vol-460
}}
==A Fuzzy Ontology for the Classification of Crowds at Concerts==
https://ceur-ws.org/Vol-460/paper02.pdf
A Fuzzy Ontology for the Classification of
Crowds at Concerts
Stefania Bandini, Sara Manzoni, and Fabio Sartori
CSAI - Complex Systems & Artificial Intelligence Research Centre
University of Milano–Bicocca
{bandini,manzoni,sartori}@disco.unimib.it
Abstract. The paper presents a fuzzy ontology for the classification of
crowds according to existing theories from social sciences and bottom–
up computational approaches. The behavior and dynamics of crowds can
be studied as resulting from the behavior of huge numbers of individu-
als taking part to it and, even if theories on crowd behavior are still
open issues for several disciplines, we refer to Elias Canetti’s theory on
masses, one of the most known and explanatory of crowds behaviors and
dynamics. This work is part of an ongoing research project whose goal
is the development of decision support systems to design and manage
public spaces and events. In particular, we focus here to a collaboration
with the famous Italian singer Lorenzo Cherubini and his band, whose
aim is to develop formal and computational tools for the classification
of different crowd phenomenology that can appear during rock concerts.
One of the main contribution of this work is towards knowledge shar-
ing and exchange, since several experiences but also software platforms
are nowadays available that could better support the study, e.g. through
simulation, of crowd behavior and dynamics.
1 Introduction
The research context of this paper refers to bottom–up approaches to crowd dy-
namics that is, the study of how and where crowds form and move [10]. Several
phenomena like crowd aggregation, dispersion and self–organized movement have
been observed and studied by multiple disciplines (e.g. physics, sociology, ethol-
ogy, social and behavioral psychology). The growing interest to crowd behavior
is motivated by relevant applicative contributions for, e.g. building design, ur-
ban planning, security and safety management, among others. This work is part
of an interdisciplinary research (SCA4CROWDS, Situated Cellular Agents for
Crowds) within this context that aims at contributing towards the development
of an ontology on crowds allowing the integration of contributions coming from
several disciplines and empirical experiences (e.g. model comparison, validation,
calibration). Potential exploitations of crowd studies are towards the support
of design and management solutions for public and crowded spaces to improve
security, safety and comfort of people. SCA4CROWDS, in particular, aims at
developing formal and computational tools to support the design, execution and
14 Proceedings of ONTOSE 2009
analysis of crowds’ behavior as effect of individual interactions (e.g. physical,
social, emotional) according to Situated Cellular Agent (SCA) [1]. SCA is a
modeling and simulation framework to model and study crowd dynamics phe-
nomena with an approach based on Multi–Agent Systems (MAS) and Cellular
Automata [2] principles.
In this paper, we present the ontological framework for crowds’ study we
are developing, in which a classification and ontological description of crowds
have been proposed referring to Elias Canetti work [4]. The latter is one of the
most known theoretical contributions resulting from 40–years of empirical ob-
servations and studies from psychological and anthropological viewpoints. Elias
Canetti can be considered as belonging to the tradition of social studies that
refer to the crowd as an entity dominated by uniform moods and feelings. We
preferred this work among others (see for instance [3, 6, 11, 7]) due to its clear
semantics and explicit reference to concepts of loss of individuality, crowd uni-
formity, spatio-temporal dynamics and discharge, that could be fruitfully repre-
sented by modeling approaches like SCA and bottom-up approaches in general.
Section 4 presents the translation of the proposed conceptual model into a com-
putational one, and its implementation with Protégé in order to support classi-
fication of new instances of crowds according to Canetti’s theory. To this aim,
fuzzy logic [12] has been adopted to disambiguate crowd instances: membership
functions developed to deal with fuzzy concepts have been experimentally devel-
oped thanks to the collaboration of the Italian singer Lorenzo Cherubini and his
staff. The paper ends with some considerations about the state of the project
and future works towards the development of a decision support system based
on the integration of ontologies and bottom-up approaches to crowd simulation
software to study crowds behavior at rock music concerts.
2 A Crowd Definition Based on Canetti’s Theory
Elias Canetti’s definition of “crowd ” can be summed up as follows:
... a unic entity dominated by uniform moods and feelings; it is
characterized by the spontaneous will of growing and aggregating other
pedestrians, and has a target, that is identified as a location of the envi-
ronment or an object that all the individuals aggregated into the crowd
desire. The aggregation phenomenon describes the growing effect that
starts from an aggregative psychological impulse called the “discharge” .
The “discharge” occurs spontaneously in people and overcomes the nat-
ural social repulsive behavior of the “fear to be touched ”. On the other
side, crowd disgregation is the result of an other psychological impulse
called “panic”, rising as the result of “individulistic impulses”.
According to social sciences a crowd is not a unic body but can be composed
by sub–structures (i.e. groups), that have their role in the general behaviors and
dynamics of the crowd itself at the macro-level [8].
Proceedings of ONTOSE 2009 15
IS A KIND OF
DISCHARGE
Destructiveness
ACTS ON
CHARACTERIZES
CHARACTERIZES
Fear of being touched INDIVIDUALS Persecution
CREATES
IS
Dense Hope on Repetition
CROWD
IS Attitude to Grow
Spontaneous
DISINTEGRATES
IS IS
PANIC
CLOSED OPEN
CROWD CROWD
ACTIONS / EVENTS CAUSES
CAUSES
ERUPTION
ADJECTIVES
FEATURES
RELATION
CAUSE / EFFECT
RELATION
Fig. 1. A graphical representation of crowd classification according to Canetti.
16 Proceedings of ONTOSE 2009
Basic features of the crowd and mechanisms governing the crowd formation
and dispersion, as described by Elias Canetti, are represented in Figure 1. The
first concept that Canetti introduces in his work is “fear of being touched ”, that
affects all individuals.
“There is nothing that man fears more than the touch of the un-
known.” [...] “All the distances which men create round themselves are
dictated by this fear.”
“It is only in a crowd that man can become free of this fear of being
touched. That is the only situation in which the fear changes into its
opposite.” [“Crowds and Power” pg. 15]
This concepts corresponds to the social distance represented by several com-
putational models for pedestrian dynamics, and it refers to the fact that indi-
viduals usually avoid to stay too close to each other unless they feel themselves
part to the crowd. According to Canetti one of the main features of a crowd is
thus the lack of “fear of being touched ”. This concept is normally not explicitly
considered in computational models for pedestrian dynamics that can be found
in literature, where the social distance parameters influence individual behav-
iors in a static relationship (usually as a reduction of attractive forces directing
movements).
Crowd formation is characterized by an event called “discharge”. It creates
a crowd and it is described as a sort of psychological impulse that affects in-
dividuals that are in the same place, and can be aroused by a common desire
normally related to an event or a situation like the beginning of a large massive
event (e.g. sportive, religious or politic events) or a dangerous situation (e.g. a
blaze). Sometimes can also arise spontaneously between people that feel to have
something in common.
“The most important occurrence within the crowd is the discharge.
Before this the crowd does not actually exist; it is the discharge which
creates it. This is the moment when all who belong to the crowd get rid
of their differences and feel equal.” [“Crowds and Power” pg. 17]
This feeling that the discharge gives and that makes a group of pedestrians
a crowd, does not last forever.
“The moment of discharge, so desired and so happy, contains its own
danger. It is based on an illusion; the people who suddenly feel equal have
not really become equal; nor will they feel equal for ever.” [“Crowds and
Power” pg. 18]
Elias Canetti introduces the concept of panic as the main mechanism re-
sponsible of crowd dispersion. Panic rises as a consequence of the presence of
individualistic impulses in crowd members: people realize that are not equal to
the other and the return of fear of being touched makes the dense mass of people
to violently disgregate.
Proceedings of ONTOSE 2009 17
“Panic is a disintegration of the crowd.” [...] “The more fiercely each
man “fights for its life”, the clearer it becomes that he is fighting against
all the others who get him in.” [...] “Whilst the individual no longer feels
himself as “crowd”, he is still completely surrounded by it. Panic is a
disintegration of the crowd within the crowd. The individual breaks away
and wants to escape from it because the crowd, as whole, is endangered.”
[“Crowds and Power” pp. 26-27]
Other fundamental characteristics of a crowd, defined by Elias Canetti, are:
1. The crowd always wants to grow. Canetti specifies that the growing of a
crowd is different according to different crowd typologies and to different
situations, and he describes this phenomenon specifically for each kind of
crowd he described in his work.
2. The crowd needs a direction, a target that can be a location (as for example
a safe place), a person (for example a whipping boy), or any other mobile or
static object.
“Crowd it is in movement and it moves towards a goal. The di-
rection, which is common to all its members, strengthens the feeling
of equality. A goal outside the individual members and common to
all of them drives underground all the private differing goals which
are fatal to the crowd as such.” [“Crowds and Power” pg. 29]
The lack of a goal for the members of a crowd is one of the main causes of the
insurgence of individualistic impulses. Therefore a crowd that reaches its goal
must quickly find another target, or it probably will start to disgregate.
“A crowd exists so long as it has an unattained goal.” [“Crowds and
Power pg. 29”]
3 Crowd Classification
Open and Closed Crowds are the most generic classification including many
crowding scenarios. Elias Canetti speaks about “Open and Closed Crowds” say-
ing:
“The natural crowd is the Open crowd; there are no limits whatever
to its growth; it does not recognize houses, doors or locks and those who
shut themselves in are suspect. “Open” is to be understood here in the
fullest sense of the word; it means open everywhere and in any direction.”
[...] “In contrast to the open crowd which can grow indefinitely and which
is of universal interest because it may spring up anywhere, there is the
Closed crowd. The closed crowd renunces growth and puts the stress
an permanence. The first thing to be noticed about it is that it has a
boundary. It creates a space for itsef which it will fill.” [...] “(Closed
crowd) is protected from outside influences which could become hostile
and dangerous and it sets its hope on repetition” [“Crowds and Power
pp. 16-17”]
18 Proceedings of ONTOSE 2009
CLOSED OPEN
CROWD CROWD
DOUBLE
CROWD
Growth
STAGNATING
CROWD
Equality
and CROWD
Density
RHYTMIC
CROWD
Target CROWD
CRYSTAL
SLOW QUICK
CROWD CROWD
MASS EVOLUTION MASS EVOLUTION
NOT CONSIDERED
CONCEPT CAUSING EVOLUTION
KINDS OF CROWD
Fig. 2. A graphical representation of crowd classification according to Canetti.
Proceedings of ONTOSE 2009 19
and also
“I designate as eruption the sudden transition from a closed into an
open crowd.” [...] “A crowd quite often seems to overflow from some well–
guarded space into the squares and streets of a town where it can move
about freely, exposed to everything and attracting everyone.” [“Crowds
and Power pg. 22”]
Canetti’s classification is performed considering some key characteristics and
two possible opposite attitudes of a crowd for each of these characteristics. Some
of the characteristics considered are:
– attitude to grow;
– attributes of density and equality;
– nature of the target.
The kinds of crowd identified are (see Figure 2):
– Open and Closed Crowds mainly differ for their attitude to grow; the attitude
of Open crowds is to grow without limits, while Closed crowds are limited
into a given spatial area;
– Stagnating and Rhythmic Crowds mainly differ for the attributes of den-
sity and equality; Stagnating crowds start their aggregation process towards
density increase, while the elements of Rhythmic crowds focus on equality
to feel themselves as part of a group;
– Slow and Quick Crowds mainly differ for the nature of the target; Quick
crowds need a near target reachable in little time, while Slow crowds can
acquire also a remote goal.
4 From Theory to Practice: an Ontology for Classifying
Crowds at Concerts
The crowd model introduced above has been exploited to develop a OWL ontol-
ogy for crowds classification. This ontology has been designed and implemented
by means of Protégé, the well known standard de facto ontology editor, and ex-
ploiting fuzzy logic to represent concepts of Canetti’s crowd classification model.
The only two kinds of crowd which can be considered separately are open and
closed crowds, thus they have been defined as roots of two subtrees in the pro-
posed taxonomy (see Figure 2).
To this aim, a case study has been chosen to start: the analysis of crowds
taking part in musical concerts. These crowds are characterized by different
behaviors according to several variables: the price to pay for the event, the
location of the event, the musical genre (e.g. opera, rock music, pop music), the
duration of the event and so on. This case study allows obtaining quite easily
quantitative information necessary to characterize crowds (e.g. the number of
people, the medium density and so on).
20 Proceedings of ONTOSE 2009
The first step in the definition of such ontology has been the clear identifi-
cation of significant features in the Canetti’s theory. Starting from the analysis
of this work as explained in previous section, these characteristics have been
summed up as follows:
– spatial limitation, which can assume the value present (e.g. if the crowd is
inside a building like a stadium), absent (e.g. if the crowd is located in
open space like a park) or not influent (if this feature is not important to
characterize the kind of crowd);
– attitude to grow that can be high (if new individuals tend to increase the
crowd population continuously), medium, low (if new individuals tend to
increase the crowd population rarely) or not influent (if this feature is not
important to characterize the kind of crowd);
– density that can be high (if the number of individuals per unit of space is
greater than a given threshold), medium, low (if the number of individuals
per unit of space is smaller than a given threshold) or not influent (if this
feature is not important to characterize the kind of crowd);
– movability, which can assume the value present (e.g. if the individuals of
the crowd move according to external solicitations, like e.g. a rock concert),
absent (e.g. if external solicitations to move are not present of captured by
the crowd, like e.g. a scientific conference) or not influent (if this feature is
not important to characterize the kind of crowd);
– duration that can be high (if the crowd disappears after a long period of
time), medium, low (if the crowd disappears after a short period of time)
or not influent (if this feature is not important to characterize the kind of
crowd);
– target closeness which can be near (if the crowd goal will be reached in a
while), far (if the crowd goal will not be reached in a while) or not influent
(if this feature is not important to characterize the kind of crowd).
Open Closed Stagnating Rhytmic Slow Quick
Spatial Limitation absent present - - - -
Attitude to grow high med/low - low high med/low
Density - - high/med low high/med low
Movability - - absent present - -
Duration - - - med/low high low
Target closeness - - - near far near
Table 1. Relations among crowd features and kinds of crowd: the symbol - means not
relevant
Table 1 summarizes the relations among these feature and the main kind of
crowd described by Canetti. A deeper analysis of such features allows pointing
out interesting relationships among the different types of crowd, for which there
Proceedings of ONTOSE 2009 21
exists some intersections depending on the value of specific attributes. In par-
ticular, in order to characterize these intersections, it is important to analyse
the values of attitude to grow, density, duration and target closeness attributes.
In fact, while it is simple to evaluate spatial limitation and movability, since
they can only assume boolean values, the comparison of others is more com-
plicated due to their level of uncertainty that make difficult to establish which
category a crowd belongs to. For this reason, our ontology exploits fuzzy logic
to describe the values of the four uncertain features. Membership functions have
been experimentally designed on the basis of the case study.
Definition 1 (Attitude to grow) The membership functions for the attitude
to grow concept are defined as follows:
⎧
⎪
⎪ 1 if x<5
⎪
⎪
⎪
⎨
10 − x
ylow = if 5 ≤ x ≤ 10
⎪
⎪ 5
⎪
⎪
⎪
⎩
0 if x > 10
⎧
⎪ 0 if x<8
⎪
⎪
⎪
⎪
⎪
⎪ x−8
⎪
⎪
⎪
⎨ 5 if 8 ≤ x ≤ 13
ymedium =
⎪
⎪ 18 − x
⎪
⎪ if 13 < x ≤ 18
⎪
⎪
⎪
⎪ 5
⎪
⎪
⎩
0 if x > 18
⎧
⎪
⎪ 0 if x < 15
⎪
⎪
⎪
⎨
x − 15
yhigh = if 15 ≤ x ≤ 20
⎪
⎪ 5
⎪
⎪
⎪
⎩
1 if 20 < x ≤ 30
where x is the number of people added to the crowd in a minute
Definition 2 (Density) The membership functions for the density concept are
defined as follows:
⎧
⎪
⎪ 1 if x < 2
⎪
⎪
⎨
ylow = 3 − x if 2 ≤ x ≤ 3
⎪
⎪
⎪
⎪
⎩
0 if x > 3
22 Proceedings of ONTOSE 2009
⎧
⎪
⎪ 0 if x < 2
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪ x − 2 if 2 ≤ x < 3
⎪
⎪
⎨
ymedium = 1 if 3 ≤ x ≤ 4
⎪
⎪
⎪
⎪
⎪
⎪
⎪ 5 − x if 4 < x ≤ 5
⎪
⎪
⎪
⎪
⎪
⎩
0 if x > 5
⎧
⎪
⎪ 0 if x < 4
⎪
⎪
⎪
⎨
x−4
yhigh = if 4 ≤ x ≤ 6
⎪
⎪ 2
⎪
⎪
⎪
⎩
1 if 6 < x ≤ 9
where x is the number of people per m2
Definition 3 (Duration) The membership functions for the duration concept
are defined as follows:
1
ylow (x, 1.5, 0.5) = 2
x − 1.5
1+
0.5
1
ymedium (x, 5, 2) = 2
x−5
1+
2
1
yhigh (x, 18, 6) = 2
x − 18
1+
6
where x is the duration an event expressed in hours
Definition 4 (Target closeness) The membership functions for the target close-
ness concept are defined as follows:
1
ynear (x, 4.3, 2) = 2
x − 4.3
1+
2
1
yf ar (x, 110, 50) = 2
x − 110
1+
50
where x is the timing necessary to reach the target expressed in minutes.
The definition of the membership functions above has allowed implementing
a software to automatically classify an instance of crowds starting from quan-
titative data quite easy to acquire. This software has been integrated into the
Protégé ontology (see Figure 3).
Proceedings of ONTOSE 2009 23
Fig. 3. The Protégé interface with an example of concept fuzzification.
5 Concluding remarks
In this paper we have presented an ongoing research project aiming at the devel-
opment of a computational framework to analyse the behavior of crowds, based
on the integration of ontologies and SCA approaches.
The crowd classification is the first step: with reference to the theory of
crowds by Elias Canetti, the main concepts to characterize instances of crowds
have been identified as well as the possible categories. Then, a distinction has
been made between crisp concepts, like movability and spatial limitation and un-
certain ones, which are attitude to grow, density, duration and target closeness.
This distinction is the key to the crowd classification: by means of fuzzy logic for
membership functions have been implemented to establish the degree of truth
of a specific instance of crowd to the Canetti’s kinds of crowd. The membership
functions have been experimentally defined, through the examination of differ-
ent types of musical events. The function have been exploited to implement a
software that has been integrate into Protégé, obtaining a sort of fuzzy classifier.
This classifier will be soon integrated with an existing SCA platform for
the simulation of crowd behavior: in this way, starting from quantitative and
objective information (like e.g. the number of people at the concert or how
the density change form a point to another according to the singer set list)
should allow implementing useful functionalities both from the organizational
and security point of views: about the organization, the possibility to simulate
how the crowd behavior change according to external factors will be very useful
to decide how managing the concert evolution; about the security, the possibilty
24 Proceedings of ONTOSE 2009
to simulate the crowd behavior before the event begins will allow identifying the
most probable critical points to monitor in order to guarantee audience safety
and order.
These functionalities address future work, thank to the collaboration with
the staff of the Italian singer Lorenzo Cherubini and districts of Italian police.
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