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Architectural evaluation of simulated
pedestrian spatial behaviour
a
a
Arash Jalalian , Stephan K. Chalup & Michael J. Ostwald
b
a
School of Electrical Engineering and Computer Science , The University of
Newcastle , Callaghan, 2308, Australia
b
School of Architecture and Built Environment , The University of
Newcastle , Callaghan, 2308, Australia
Published online: 31 May 2011.
To cite this article: Arash Jalalian , Stephan K. Chalup & Michael J. Ostwald (2011) Architectural evaluation
of simulated pedestrian spatial behaviour, Architectural Science Review, 54:2, 132-140
To link to this article: http://dx.doi.org/10.1080/00038628.2011.582372
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Architectural evaluation of simulated pedestrian
spatial behaviour
Arash Jalalian1*, Stephan K. Chalup1 and Michael J. Ostwald2
1
Downloaded by [Columbia University] at 05:03 15 November 2014
2
School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan 2308, Australia
School of Architecture and Built Environment, The University of Newcastle, Callaghan 2308, Australia
The purpose of the software system described in this article is to support architects and urban designers to better assess the
impact of pedestrian behaviour on planned urban spaces and streetscapes. A new method for modelling and simulating pedestrians in urban environments is proposed. It analyses pedestrian behaviour with a combined focus on movement trajectories, walking speed and the angle between the movement vector and the gaze vector of individuals in large groups of
simulated pedestrians. The system learns a statistical model characterizing normal behaviour, based on sample observations
of regular pedestrian movements without the impact of significant visual attractions in the environment. Sudden changes of
the pedestrians’ behavioural characteristics, caused by the visual detection of ‘attractive’ objects, are considered as abnormal behaviour. The simulated environment can be automatically generated using scanned line drawings of two-dimensional
street maps or public spaces. In the simulation model, a variety of scenarios can be defined and modified by altering different
parameters. Using the example of Wheeler Place in Newcastle (Australia), our pilot experiments demonstrate how pedestrian
behaviour characteristics can depend on selected abstract features in urban space. Among 10 different example scenarios,
the analyser was able to identify the most frequently visited positions associated with attractive objects.
Keywords: Design evaluation; human behaviour analysis; pedestrian behaviour modelling
INTRODUCTION
The analysis of pedestrian flow dynamics in indoor and
outdoor areas is an important facet of architectural and
urban design. While a range of software programs have
been developed in recent years to model idealized pedestrian
flow between entry and exit points in a plan, such software
typically neglects to consider the impact of ‘attractors’ and
of human gaze dynamics. In ‘public’ places, including
both external environments (streets and plazas) and internal
spaces (malls and museums), ‘attractor’ objects, like billboards or display stands, distract pedestrians from following
a direct path towards their destinations. While there are a
range of possible names for these visual distractions, in the
present article they will be described as ‘attractive objects’.
It can further be assumed that these objects have different
levels of attraction: some will attract a pedestrian’s gaze,
while others will completely interrupt their passage. An
object with a low level of attraction, such as a piece of
writing on the wall, would typically not attract pedestrians
who walk fast. However, by placing objects with higher
levels of attraction into the scene it is possible to reduce
the speed of pedestrians, making them susceptible to being
attracted to objects with lower levels of attraction. The
importance of this type of analysis is that it can be a useful
predictive and analytical tool not only in architecture and
urban design, but also in crowd management, transport
facilities management, crime prevention, disaster planning,
marketing and epidemiology. Thus, the ability to predict
the response of a pedestrian in an urban area to his or her surroundings is important in estimating the effects of changes in
the built environment.
The reality is that, contrary to most software simulations,
pedestrians do not always follow a simple path along the line
connecting origin to destination. Furthermore, in contrast to
vehicular flows, which circulate along fixed corridors of the
road environment and are subject to specific traffic rules,
pedestrian flows are characterized by a significant degree
of randomness, so that one could consider each individual’s
trip as unique (Yannis and Golias, 2009). Simulating and
analysing such a big data set of behaviours calls for an
advanced intellectual method involving many ingredients
such as navigation and orientation, evaluation and decision
making, variation of personal behaviour and crowding.
Therefore, computational tools are crucial for mapping this
randomness into a real-time model that simulates pedestrian
behaviour. Regression models (Older, 1968), gravity model
formulations (Ness et al., 1969), doubly constrained models
*Corresponding author: Email: arash.jalalian@uon.edu.au
ARCHITECTURAL SCIENCE REVIEW 54 | 2011 | 132–140
doi:10.1080/00038628.2011.582372 #2011 Earthscan ISSN: 0003-8628 (print), 1758-9622 (online) www.earthscan.co.uk/journals/asre
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Pedestrian spatial behaviour simulation 133
(Hagishima et al., 1987), generic coupled differential
equations (Gloor et al., 2003) and discrete choice models
(Antonini et al., 2006) are all examples of applied analytical
computational models in pedestrian behaviour simulations.
In analytical models, changes in pedestrian behaviour are
expressed as a mathematical function that controls the
average pedestrian movement. Since the analytical models
apply the same rules to all simulated individuals and
perform a simulation based on average characteristics, they
are restricted in simulating pedestrian behaviour, which
in reality would vary much more depending on different
plans, personal behavioural characteristics and social
preferences.
One of the useful ways of modelling pedestrian behaviour
and overcoming the shortcomings of analytical models is to
develop an agent-based simulation. An agent-based simulation is a computational model for modelling the behaviour
of autonomous agents. In agent-based modelling, a system is
modelled as a collection of autonomous decision-making
entities called agents (Bonabeau, 2002). For an architectural
or urban simulation to be useful for designers, agents should
mimic real pedestrians and they should be of a number of
different types with different movement abilities. It also
should be possible to change their characteristics and
numbers to fit the circumstances being examined. Considerable research has been done on the topic of multi-agentbased simulation systems (Bonabeau, 2002; Yan and
Kalay, 2006; Yannis and Golias, 2009). For instance,
researchers in Catania (Francesca Camillen et al., 2009) performed an agent-based simulation of people visiting and
evacuating Planimetry of Castello Ursino in emergency
situations. They modelled the environment in 2D using
the NetLogo platform, which is a multi-agent simulation
software (Wilensky, 1999). Francesca Camillen et al.
(2009) confirmed the usefulness and effectiveness of agentbased simulations in the design and analysis of complex
social systems. Such simulations are used to support more
traditional strategies already available to engineers. For
example, Gipps and Marksjo (1985) presented a macrosimulation cellular approach to model interactions between
pedestrians, which is intended for use in graphical computer
simulation. In agent-based pedestrian simulations, cellular
automata and intelligent agents have been used extensively
in recent years (Burstedde et al., 2001; Francesca Camillen
et al., 2009; Wang and Chen, 2009). In another example,
the team of Kazuhiro et al. (2007) proposed the use of real
coded cellular automata as a new numerical model for
pedestrian dynamics. They obtained the critical number of
people beyond which clogging appears at exits of rooms.
In all cellular approaches, researchers have assumed that in
each cell only one agent can be placed. Cellular approaches
ease the way for simulating pedestrians; however, they also
reduce the accuracy of pedestrian models in urban areas.
While agent movements in cellular models are limited by
cell sizes, in the real world pedestrians are not limited to
follow cells and can choose their next step in any direction.
Placing virtual pedestrians in architectural space aids in
analysing the effectiveness of the space and in improving
the design of the space. Thiele (2002) discussed the importance of virtual architecture in design and using the computer
world as a suitable medium through which designers can
convey the human side of design. To demonstrate the idea
of using simple intelligence instead of artificial intelligence,
an outline of Cura, a virtual presenter, was presented by
the author (Thiele, 2002). Simulating the behaviour of pedestrians in ‘normal’ situations is also important in urban
planning (Jiang, 1999), land use (Parker et al., 2003) and
traffic operations (Cetin et al., 2002). By analysing this behaviour in different spaces, the use of these spaces can be
assessed. Behaviour normality analysis is an active research
topic in increasing the security of public places such as
museums, parks and cinemas. One of the most popular
methods of detecting abnormal behaviours is by comparing
behavioural characteristics with the most frequently observed behaviours in the past. Utilizing advanced technology
increases our computational abilities to employ complex
algorithms for comparing pedestrians’ behavioural characteristics. For instance, Suzuki et al. (2007) proposed a
computational method to learn motion patterns and detect
anomalies by human trajectory analysis. They employed
hidden Markov models (HMMs) to model time-series
features of human positions. Using a similarity matrix of
HMM mutual distances and k-means clustering (a method
to partition n observations into k clusters), they acquired
features of human motion patterns.
In the present article we simulated a multi-agent system
with the characteristic dynamics of a crowd of moving pedestrians in a section of an abstract urban two-dimensional
(2D) environment. A similar approach to 2D mapping of
environments has previously been proposed (Leal et al.,
2006), and it has also been used in robotics for the exploration of an office-like indoor environment using a multi-robot
team (Lau, 2003). Our approach is based on artificially generated abstract point-like agents. In the proposed agent-based
pedestrian behaviour simulation, each agent individually
assesses its current situation and makes decisions on the
basis of its current state and a set of rules. In the simulation,
agents may carry out various behaviours resembling realworld pedestrian behaviour in an urban streetscape. The
simulation generates a set of behavioural characteristics –
for example, walking, running, standing, getting attracted
to an obstacle and associated changes of gaze direction.
The developed analyser software evaluates the simulated
behaviours in order to identify the impacts of different
walking environments on pedestrian behaviours. The analyser uses a combination of machine-learning classifiers
and statistical algorithms to allow it to learn past normal
behaviours and distinguish between normal and abnormal
behaviours in future data.
To demonstrate the software developed by the authors, a
simulation using the plan of an urban space called Wheeler
Place is presented. This space, located between the two
ARCHITECTURAL SCIENCE REVIEW
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134 Jalalian, Chalup and Ostwald
busiest streets in Newcastle (Australia), features a constellation of attractive objects and offers an excellent test
environment for analysing different pedestrian behaviours
(Figure 1). The space is next to Newcastle’s Civic Theatre
and City Council Chamber, and these two busy buildings
have made this area one of Newcastle’s most crowded
public areas. There are also several places of different
levels of attraction in this area itself, including the City of
Newcastle Information Centre and Climate Meter, Juicy
Beans Restaurant and Internet Cafe, a big public art work,
the Civic Theatre and Civic Theatre Restaurant. In combination, these offer several destinations for pedestrians who
can be differentiated in behavioural characteristics, physical
characteristics and personal preferences. As shown in
Figure 1, the vicinity’s five entrances (black squares) and
exits (red exit signs) are restricted to several distinguishable
points, and pedestrians mainly choose one of these points to
enter and exit Wheeler Place.
The present article is an expanded and revised journal
version of a conference paper (Jalalian et al., 2010b) and continues our previous work presented in Jalalian et al. (2010a).
PEDESTRIAN MODEL
Figure 2 demonstrates our agent model in the proposed
multi-agent-based pedestrian simulation. In the real world,
individuals have several characteristics representing their
spatial behaviours in an urban streetscape such as speed
of movement, head direction and location. Table 1 describes
the parameters we used for modelling a pedestrian’s
behaviour.
Figure 1 | Wheeler Place simulation in scenario 1
ARCHITECTURAL SCIENCE REVIEW
Figure 2 | Simulated agent: UFOV (grey area), speed vector
(short line), gaze direction (long line) and the angle between
them (a)
At the start of the simulation, each agent is initialized
using the described parameters. Some parameters are generated randomly and others are calculated based on those ranj
i
domly generated. In the initialization step endx;y
and startx;y
Table 1 | Pedestrian model parameters
Parameter
Description
Unit,
symbol
Location
Location of an agent in the urban
plan at time
pixel, (xt,
yt)
Speed
Speed of an agent at time t,
determined by speed category
and location of the agent relative
to its surroundings
pixel, dx dy
,
dy dt
or (b, s)
Angle
Angle between head direction
and speed vector at time t
degree, at
Field of
view
Standard pedestrian’s
functional or useful field of view
(UFOV), which is assumed to be
958 (Ball and Owsley, 1991)
degree,
UFOV
Sight
Random number that represents
an agent’s visual ability. It is
associated with the agent’s
sight category
pixel, V
Starting
point
First location of the agent in the
plan
pixel,
startix,y
Destination
Final destination where the
agent is going to leave the urban
area
pixel,
endix,y
Speed
category
Five different speed categories
to model different pedestrian
behaviours subject to speed
Scat
Sight
category
Five different sight categories to
model different pedestrian
behaviours subject to vision
capability
Vcat
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Pedestrian spatial behaviour simulation 135
are generated based on random values, and one start point
and one end point are selected among the defined start
points and end points in our urban model. In the urban
model we have different categories of behaviours that are
defined by Scat and Vcat. In each category, we have varieties
of behaviours defined by a random function. To separate the
behaviour of each category from others, in each category the
range of randomness is restricted by defined maximum and
minimum values. After initializing Scat and Vcat for each
agent, V and ksk are calculated based on Vcat and Scat,
respectively.
In the simulation, when there are no stimuli to attract
pedestrians’ attention in urban space, the angle a, controlling
gaze direction, remains a small random value. This is because under normal conditions, pedestrians would typically
look straight ahead such that the gaze vector can be assumed
to be almost parallel to the speed vector. In all other conditions, a will be changed accordingly. The aim of each
i
agent is to reach endx;y
, and therefore the direction of movement, b, is determined by a random value and the direction
i
i
of an imaginary line passing through endx;y
and startx;y
.
URBAN MODEL
In our simulation the urban model was obtained by using a
scan of a plan of Wheeler Place. As shown in Figure 1,
the considered area has five entrances and exits
j
i
ðstartx;y
; endx;y
: i; j ¼ 1; . . . ; 5Þ. Each attractive object is
surrounded by a circle that indicates its assumed level of
attraction. The radius corresponds to the attraction level. In
the reported simulation experiments, we have selected five
positions (Pos#1, Pos#2, . . . , Pos#5) where attractive
objects can be placed (see Figure 2). During simulation,
once an agent’s UFOV overlaps with the attraction area of
an object the agent will move towards the object with the
maximum allowed speed in its speed category Scat. Therefore, objects with higher levels of attraction can attract
agents with the same Vcat from further distances. On the
other hand, agents with bigger V can be attracted by
further objects with the same level of attraction.
In the urban model, the following terms are used: image
plan is an aerial one-point perspective plan of the area,
start points are the locations of the entrances, end points
are the locations of the exit points, object location indicates
location of the attractive object on the image plan (objx,y),
object visit counter shows the number of agents that have
been attracted to the object and finally object attraction
level defines the level of attraction for an attractive object.
We have also assumed that boundaries of pathways and
other obstacles such as trees are impermeable.
BEHAVIOUR SIMULATION
In the simulation, each agent assesses its behaviour according to several decision-making rules defined by the
pedestrian model and the urban model. We defined two
major types of agents: attracted agents and normal agents.
While the former is attracted to an attractive object, the
latter has not seen any attractive object and just moves
along the normal direction of movement towards its destination. Normal directions of movement are defined by the lines
between start points and destinations. Each agent has
selected one normal direction in the initialization stage.
Agents may carry out various behaviours roughly resembling real-world pedestrian behaviour in an urban streetscape
such as walking, running, standing or becoming attracted to
an obstacle. Regarding pedestrians’ reaction to attractive
objects in an urban area, pedestrians may have four different
spatial behaviours: becoming attracted (when they can see
the object for the first time, and believe the object is attractive for them), not becoming attracted (when they can see
the object for the first time, and believe the object is not interesting enough for them to move closer to it), visiting (stopping while they are studying the object) and normal (when
they cannot see any attractive object). In the simulation,
various behaviours are defined by changing an agent’s
characteristic parameters. To obtain the next location for
each agent, we first calculate the direction of movement,
btþ1 , as follows:
!
8
i
>
end
x
t
>
x
1
> tan
þ rnd bosc ;
>
>
>
endyj yt
>
>
>
>
>
normal/not becoming attracted
<
btþ1 ¼ bt ; visiting
!
>
>
>
i
>
obj
x
t
>
x
1
>
tan
þ rnd bosc ;
>
>
>
objjy yt
>
>
:
becoming attracted
Here rnd is a random value in [0,1] at time t. Since pedestrians do not walk exactly on a straight line, we simulated
this behaviour by using bosc ¼ 208, which indicates the
oscillation range for b. When the UFOV for an agent overlaps with the circle indicating an object’s level of attraction,
the simulated agent will be notified of the attractive object.
In this case, the agent uses the ‘becoming attracted’ equation
to calculate btþ1. By using this equation, the agent moves
towards the object instead of the end point. When the
agent comes close enough to the object, it will spend some
time standing at the object and studying it. During this
period (‘visiting’), the direction of movement remains constant. At all other times, the agent shows normal spatial behaviour and uses the ‘normal’ equation to calculate btþ1 . In a
real-world scenario, while pedestrians are moving, they do
not always look straight ahead. To simulate this behaviour,
we defined aosc as the oscillation range for the relative gaze
angle at in normal behaviour, which is calculated as follows:
at ¼ ðrnd 2 aosc Þ aosc
In ‘not becoming’ and ‘becoming attracted’ behaviours,
the agent’s gaze vector points to the attractive object.
ARCHITECTURAL SCIENCE REVIEW
136 Jalalian, Chalup and Ostwald
To obtain the next location for each agent, we also need to
calculate the speed of movement, ksk. In the simulation, we
used five different speed categories for each agent. Each category has a maximum speed and a minimum speed. Agents
choose their speed category when they are initialized, and
this category remains constant during the agent’s lifespan.
ksk is obtained as follows:
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81
>
s ðrnd þ scat 1Þ;
>
>
> 5 max
>
>
>
normal/not becoming attracted
<
ksk ¼ 0; visiting
>
>
1
>
>
>
rndðsmaxscat Þ;
>
>
:5
becoming attracted
where smax is the maximum possible speed and rnd is a
random value in [0,1]. By applying btþ1 ; ksk and ðxt ; yt Þ
in the following equations and solving them for
ðxtþ1 ; ytþ1 Þ, we obtain the next location for each agent:
8
2
xt Þ2 þ ðytþ1 yt Þ2
< ksk ¼ ðxtþ1 x xt
: btþ1 ¼ tan1 tþ1
ytþ1 yt
Since we assumed boundaries of pathways and other
obstacles such as trees to be impermeable, we developed
an algorithm to extract the boundaries and obstacles from
the image plan using image processing techniques. We
applied Otsu’s method, which chooses the threshold to minimize the intraclass variance of black and white pixels, to
extract objects from the image plan (Otsu, 1979). The
extracted boundaries and obstacle information are then
used to validate the obtained next location for each agent
ðxtþ1 ; ytþ1 Þ.
We have developed the simulation software, the analyser
(described in the next section) and their graphical user interface (GUI) in the MATLAB R2010a environment. Our GUI
provides several tools to change different parameters of
simulation. This includes changes in the maximum number
of agents in the scene, methods of training the classifiers,
image plan, start points, end points, number of speed categories, number of sight categories, levels of attraction for
attractive objects, animation settings and visualization settings. The interface also provides the ability to save a
running simulation and load it in the future. This helps
designers to change a variety of settings and examine the
impacts of those settings on the same design. Employing
MATLAB and developing the simulation software from
scratch enables us to store the simulation results in any
desired data format. This provides enough flexibility for
other software developers to use our simulation results in
other software and programming platforms.
ARCHITECTURAL SCIENCE REVIEW
PEDESTRIAN BEHAVIOUR ANALYSER
In order to analyse pedestrian behaviour, we have previously
proposed and applied a new approach for outlier detection
(Jalalian et al., 2010a). This approach utilizes a combination
of one-class support vector machines (SVMs) and dynamic
time warping (DTW) to identify pedestrians who show
abnormal changes in their direction of movement, gaze
direction or walking speed. Our system analyses pedestrian
behaviour with a combined focus on ðxt ; yt Þ; bt ; ksk; at
and d at =dt of individuals in large groups of simulated pedestrians. The system learns a statistical model characterizing
normal behaviour, based on sample observations of regular
pedestrian movements without the impact of significant
visual attractions in the environment. Irregular pedestrian behavioural characteristics, mainly caused by detection of visually attractive objects, are considered as abnormal behaviour.
We employed one-class SVMs combined with DTW
to separate agents into two classes: attracted agents and
normal agents. One-class SVMs are able to differentiate
one class of data from the rest of the data in the input
space based on similarities (Vapnik, 1998). They are useful
when we have either only positive or only negative data
because they are trained on one group of data only. Since
in the simulation attracted agents may show unpredictable
behaviours, it would be impractical to train classifiers with
their behavioural data. Therefore to detect outliers and distinguish between normal samples and attracted samples,
we fed normal data to the classifiers. Our implementation
uses SVMs with Gaussian kernel function as provided by
the LIBSVM toolbox (Chang and Lin, 2009).
Classification of noisy features can be a serious challenge.
To keep the false detection rate as low as possible, we
applied DTW combined with SVMs. DTW is a popular
method that has been used for comparing two different
sound spectrums with different lengths (Sakoe and Chiba,
1978; Keogh and Pazzani, 2001). The ability of DTW to
compare two different signals and calculate the distance
between them makes it a suitable tool for comparing the
complete sequences of locations, speeds, angles and their
derivations.
EXPERIMENTAL RESULTS
Although human walking speed can vary greatly depending
on factors such as height, weight, age, terrain, surface, load,
culture, effort, gender and fitness, it can be categorized into
five distinguishable ranges in urban areas (Burnfield and
Powers, 2006). Figure 4 shows five different speed categories and their typical random characteristics for five
agents in our simulation. As shown in this figure, there is
a considerable difference in speed, ksk, among different
speed categories. Each agent moves with a random speed
within the boundaries of its speed category. Using different
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Pedestrian spatial behaviour simulation 137
categories of speed and sight simulates different spatial
behavioural characteristics.
The experiment shown in Figure 3 displays the simulated
agents’ trajectories. In this example, we used the five speed
categories and distinguished them by colour coding in the
Hue saturation value colour system. From left to right, the
colour bar at bottom of Figure 3 represents speed categories
with higher speed. In this figure each circle represents an
attractive object. The number of agents attracted to each
object is shown in the centre of the circles. The number of
agents that have been attracted to an object depends on
many factors such as level of attraction for the object, and
location of the object relative to start points, end points
and other objects.
Figures 5 and 6 illustrate typical simulated behavioural
characteristics for an agent. As shown in this figure, although
the agent could see two objects, it was attracted to the first
one but not to the second one. The period that the agent
was attracted by an attractive object is called the visiting
period (dark grey areas in the figures). During this period
the agent was not moving and most of the behavioural
characteristics remain constant. Just before the visiting
period there is the ‘becoming attracted’ period. In this
period the agent moves towards the object with highest
allowed speed defined in its speed category (Figures 5
and 6). The b curve also shows a significant change during
this period, which represents change in direction of movement. Figure 7 highlights trajectories for normal and
attracted agents in scenario 10. The type of each trajectory
is identified by the analyser using simulated values for
ðxt ; yt Þ; bt ; ksk; at and d at =dt. Previous approaches often
employed two-class classification of normal and abnormal
data. However, this can increase the error rates when only
a small proportion of all possible abnormal behaviours can
be observed (i.e. the data are unbalanced). In our pilot experiments we used one-class SVMs and achieved an accuracy of
93% (Jalalian et al., 2010a).
Figure 3 | Colour-coded speed representation in scenario 2
Figure 4 | Five agents with five different speed categories
While the actual level of attractiveness of an object, to any
given person, cannot be predicted with any accuracy, we can
make several informed assumptions about object attractiveness to support the early stages of simulation testing. For
example, as discussed in the introduction, at Wheeler Place
there are five obvious locations that can represent attractive
objects, including the art work and the Civic Theatre
(which can be regarded as highly attractive and the rest as
less attractive). In Figure 1, we have indicated five positions
in the plan to place five attractive objects. We assume that
there are two levels of attraction: ‘high’ and ‘low’. Table 2
lists 10 possible scenarios that were considered in our simulation experiments of Wheeler Place. Because we have five
positions to put place five objects, two of which have a
Figure 5 | Simulated behavioural parameters for an agent:
visiting period (dark grey area), becoming attracted period
(light grey area), and normal behaviour (white area)
ARCHITECTURAL SCIENCE REVIEW
138 Jalalian, Chalup and Ostwald
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Table 2 | Possible scenarios for two categories (low, high) of
attractive objects in Wheeler Place
Figure 6 | Simulated behavioural parameters for an agent:
visiting period (dark grey area), not becoming attracted
period (patterned area) and normal behaviour (white area)
low level of attraction and three with a high level of attraction, the number of permutations for placing objects in positions without repetition is 5!/(3! 2!) ¼ 10.
Figure 8 shows the probability of attraction for different
positions in all possible scenarios with respect to the
number of simulated agents. The results shown in this
figure were obtained by simulating 5000 pedestrians for
each scenario. As shown, the fourth position has the
highest probability of attracting agents in all scenarios.
Placing a highly attractive object in this position and a less
attractive object in the fifth position (as in scenario 2) will
balance attracted crowds on both sides of Wheeler Place.
The last column of Table 2 shows the probability of attracting a pedestrian to an attractive object in Wheeler Place.
Figure 7 | Normal (black) and attracted (grey) trajectories of
agents in scenario 10
ARCHITECTURAL SCIENCE REVIEW
Scenario
Pos#1
Pos#2
Pos#3
Pos#4
Pos#5
1
Low
High
Low
Low
High
2
High
Low
Low
High
Low
3
High
Low
High
Low
Low
4
Low
Low
High
High
Low
5
Low
Low
High
Low
High
6
Low
High
High
Low
Low
7
Low
High
Low
High
Low
8
Low
Low
Low
High
High
9
High
High
Low
Low
Low
10
High
Low
Low
Low
High
These simulations demonstrate that arranging the objects
based on scenario 2 will result in the highest number of
attractions compared with the other scenarios.
DISCUSSION
Pedestrian spatial behaviours depend on different plans,
personal behavioural characteristics and social preferences.
Using analytical models (Gloor et al., 2003) to simulate
these behaviours based on average characteristics leaves a
considerable proportion of behaviour categories unexplained.
Using multi-agent-based cellular models, on the other hand,
to overcome this problem has its own difficulties. In the real
world, pedestrian movements include a great variety of
speeds and directions. Using cellular approaches (Burstedde
Figure 8 | Probability of attraction for different positions in
different scenarios
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Pedestrian spatial behaviour simulation 139
et al., 2001; Francesca Camillen et al., 2009; Wang and
Chen, 2009) to model pedestrian spatial behaviours restricts
this variation to several available cells for each agent. The
cellular approaches restrict freedom of choice in direction
at each step and agents’ speed can only be generated based
on cell sizes. In contrast, in our approach agents can be
designed with a high degree of freedom. They can have a
wide variety of speeds and directions at each time step (as
shown in Figures 3, 5 and 6).
The fact that eye movements are linked by attention does
not mean that these two systems are completely mutually
dependent. Observers can direct their visual attention to
different areas of visual space even while the eyes remain
fixed. Thus, the relationship between attention and eye
movements is one of partial interdependence (Jalalian
et al., 2011). Attention is free to move independently of
eyes, but eye movements require visual attention to precede
them to their goal. While pedestrians are walking in an
urban space, their eyes always scan their surroundings using
saccadic eye movements. These saccadic eye movements
occur at a rate of about 3–4 per second (Jalalian et al.,
2011). This explains why sight category and a (the angle
between movement direction and gaze vector) are used to
analyse pedestrians’ reaction to their surrounding area, including attractive objects. Future extensions of the described simulation software will also include phenomena such as dynamic
attractive objects and crowd attraction (Jalalian et al., 2011).
spatial behavioural analysis in urban space. The simulation
adds two unique features to the conventional model: gaze
vector and attractor objects. The benefits of this new
approach become especially clear in cases of complex
spatial arrangements where changing the configuration of
walking environments (and thus adapting designs) is possible. A software system and its GUI were developed by the
authors to read plans, extract boundaries and obstacles
from them, run the simulation and then analyse behavioural
characteristics of simulated agents. The GUI offers a userfriendly environment for architects who may not be familiar
with complex computer programming problems. The simulation was run for a real-world space, Wheeler Place in Newcastle. Different scenarios with different configurations and
their impacts on pedestrian behaviour were presented and
discussed. The experiments show that an approximate behavioural model has been produced to evaluate the
authors’ behavioural analysis system. The system can be
used to demonstrate that changes in the configuration of
the physical/visual built environment can be reflected by
measurable changes in agent behaviour. Finally, the
authors are planning to employ the analyser system
described in this article for examining real-world
video data collected by a pedestrian detection and tracking
system.
ACKNOWLEDGEMENT
CONCLUSION
This study describes the development and testing of a
new multi-agent-based software simulation for pedestrian
This research was supported by ARC DP1092679 ‘Modelling and predicting patterns of pedestrian movement: using
robotics and machine learning to improve the design of
urban space’.
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