First lesson and introduction of the PhD course on "Computational approaches to Physical and Virtual Crowd Phenomena" - titled "Simulation of complex systems: the case of crowds"

1. Simulation of complex systems:
the case of crowds
Giuseppe Vizzari
Complex Systems and Artificial Intelligence Research Center (CSAI)
University of Milano-Bicocca, Italy

2. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

3. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

4. Complex Systems?
• Several (more ore less formal) definitions:
• A complex system is a highly structured
system, which shows structure with variations
• A complex system is one whose evolution is
very sensitive to initial conditions or to small
perturbations, one in which the number of
independent interacting components is large,
or one in which there are multiple pathways by
which the system can evolve
• A complex system is one that by design or
function or both is difficult to understand and
verify
• A complex system is one in which there are
multiple interactions between many different
components
• Complex systems are systems in process that
constantly evolve and unfold over time
• …
• Features of complex systems:
• Composed by several interacting elements
• Nonlinearity
• Networked or hierarchical structure
• Positive and negative feedbacks
• Possibility to evolve and adapt
• Robustness and plasticity
• …
• Complex systems research is a hot topic
for scientists… but also for engineers!
• One of their main features is their internal
structure and the interaction among their
composing parts… that very often is
studied by means of simulations

5. Non linearity?

6. Simple vs Complicated vs Complex
• Simple system
• Cause and effect relationships known, stable,
repeatable and predictable
• Known system
• Example: bicycle
• Complicated system
• No fundamental difference with respect to cause and
effect relationships, but on a much larger scale and with
increased requirements around coordination or
specialised expertise
• “Knowable" system
• Examples: car, bus, airplane, rocket
• Complex system
• Cause and effect relationships often understandable in
retrospect, but not necessarily easily reproduced and
predictable
• Systems that can (to a certain extent) be analysed by
means of simulation
• Examples: living organism (even simple as yeast),
human organisation (not just an organisational chart, a
real organisation, e.g. a Department)… a CA model (the
Game of Life)!

7. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

8. Motivations of
crowd modeling and simulation
• Designer’s decision support
• Normal and evacuation situations
• Positioning of signs
• Malls and shopping centres
• Support crowd management by means
of the elaboration of what-if scenarios
• Situations where large crowds are
frequent (sport events, festivals,
religious events)
• Public transport systems, in
particular stations
• Support the study of pedestrian
behaviour
• Envisioning of different behavioural
models in realistic environments
• Possibility to perform ‘in-machina’
experiments

9. Crowds of pedestrians as
complex systems
• Overall system behaviour depends on
individuals’ decisions and actions…
• … that are generally influenced by a
large number of factors
• … intertwined in an often
unpredictable way
• Mixed and conflicting mechanisms
• Competition for the shared space…
• … but also cooperation (non written
social norms) to prevent stall
situations
• Imitation...
• ... but also natural tendency to stay
at a distance (proxemics)
• Emergent phenomena
• …but also rules, norms
• …

10. Considerations About
Elder Pedestrians
• Elder pedestrians are frequent
• Their impact on the transportation
infrastructures can be significant
• Structures should be ready to host
them properly
• Service points should be organised
and managed taking them into
consideration
• Simulation can support the
evaluation of designs and highlight
potential issues
• New models for supporting the should
be able to deal with
• Heterogeneity in pedestrian speed
• Systematic differences in reaction
times
• Presence of groups, potentially
structured, some of which with
strong cohesion

11. Impact of groups in pedestrian
and crowd dynamics
• Current approaches generally
consider every pedestrian as a
individual with no relationships
• Considering only his/her own
goals
• Considering other pedestrians
as moving obstacles
• Nonetheless, in several situations
pedestrians are bound by
relationships influencing their
movement
• Generally speaking, a crowd is
made up of groups of
pedestrians...
• What do we miss by neglecting this
aspect of pedestrian behaviour?

12. Not just “physical” crowds

13. Simulation: a definition and
motivations

14. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model

15. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model
– to evaluate designs and plans
without actually bringing them into
existence in the real world

16. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model
– to evaluate designs and plans
without actually bringing them into
existence in the real world
– to evaluate theories and models of
complex systems by envisioning the
effect of the modelling choices, with
the aim of gaining insight of their
functioning

17. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model
– to evaluate designs and plans
without actually bringing them into
existence in the real world
– to evaluate theories and models of
complex systems by envisioning the
effect of the modelling choices, with
the aim of gaining insight of their
functioning
• The use of “synthetic environments”
is sometimes necessary, because the
simulated system cannot actually be
observed

18. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model
– to evaluate designs and plans
without actually bringing them into
existence in the real world
– to evaluate theories and models of
complex systems by envisioning the
effect of the modelling choices, with
the aim of gaining insight of their
functioning
• The use of “synthetic environments”
is sometimes necessary, because the
simulated system cannot actually be
observed
– Because it is actually being designed

19. Simulation: a definition and
motivations
• (Computer) Simulation represents a
way to exploit a computational
model
– to evaluate designs and plans
without actually bringing them into
existence in the real world
– to evaluate theories and models of
complex systems by envisioning the
effect of the modelling choices, with
the aim of gaining insight of their
functioning
• The use of “synthetic environments”
is sometimes necessary, because the
simulated system cannot actually be
observed
– Because it is actually being designed
– For ethical or practical reasons

20. Simulation life-cycle
5

21. Simulation life-cycle
• From the target system to its computational model and a simulator
Modeling
and design
of a simulator
Target System
Model and simulator
5

22. Simulation life-cycle
• From the target system to its computational model and a simulator
• Execution of a simulation campaign
Simulation execution
Modeling
and design
of a simulator
Target System
Model and simulator Data generated by the
simulation(s)
5

23. Simulation life-cycle
• From the target system to its computational model and a simulator
• Execution of a simulation campaign
• Evaluation/validation of the model (and simulator) against collected
data
Simulation execution
Dynamics of Target System
Modeling
and design
of a simulator
Analysis of results +
interpretation
(model evaluation
leading to explanation
or
prediction)
Target System
Model and simulator Data generated by the
simulation(s)
Collected Data
5

24. Simulation life-cycle
• From the target system to its computational model and a simulator
• Execution of a simulation campaign
• Evaluation/validation of the model (and simulator) against collected
data
• Possible usage for explanation and/or prediction
Simulation execution
Dynamics of Target System
Modeling
and design
of a simulator
Analysis of results +
interpretation
(model evaluation
leading to explanation
or
prediction)
Target System
Model and simulator Data generated by the
simulation(s)
Collected Data
5

25. A comprehensive framework for integrated
synthesis and analysis

26. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

27. Pedestrians and crowds: levels of analysis
Chapter 5. Identification of processes and elements in a pedestrian flow model 105
The operational level pertains to immediate decisions of the pedestrian concerning his
behaviour. Interactions with other pedestrians play an important role at this level.
An overview of the mentioned decision levels, related processes at and interactions be-
tween different decision levels are given in figure 5.1. Also, inputs and outputs on each
level are indicated in this figure.
STRATEGIC
TACTICAL
OPERATIONAL
Activity set choice
Derived
activity set
Network topology
Timetable
Route
Activity schedule
Activity areas
Route choice
Activity area choice
Activity scheduling
Dynamic network
characteristics
Geometry
Obstacles
Vehicle characteristics
Walking
Waiting
Performing an activity
Trajectory choice
Figure 5.1: Levels in pedestrian behaviour based on Hoogendoorn et al. (2001) and figure 3.1[Hoogendoorn et al., 2001]

28. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

29. Modeling approaches: what to model?
Pedestrian/Crowd
models
Macroscopic
(continuous fluids)
Microscopic
(individual pedestrians)
Overall equations (mass conservation,
flow velocity) determining velocity and
density in the analysed area
Individual state, position, decision are
modelled; aggregate measures derive
from these decisions
+ lower computational costs
for large crowd
- limited applicability
+ generally greater applicability
- higher computational costs
for large crowd

30. Macroscopic crowd models rationaleMathematical models Numerical tests Conclusion
Macroscopic models
Pedestrians as "thinking fluid"1
Averaged quantities:
⇢(t, x) pedestrians density
~v(t, x) mean velocity
Mass conservation
(
@t⇢ + divx(⇢~v) = 0
⇢(0, x) = ⇢0(x)
for x 2 ⌦ ⇢ R2
, t > 0
Two classes
1st order models: velocity given by a phenomenological
speed-density relation ~v = V (⇢)~⌫
2nd order models: velocity given by a momentum balance equation
1
R.L. Hughes, Transp. Res. B, 2002
P. Goatin (INRIA) Mathematical modeling of crowds January 12, 2014 5 / 22
Mathematical models Numerical tests
Eikonal equation: level set curves for |rx | = 1
In an empty space: potential is proportional to distance to destination
P. Goatin (INRIA) Mathematical modeling of crowds January 12, 20
[Goatin, 2014]
• Global constraints are specified, e.g.
• Mass conservation
• Velocity function over the analysed area
• Different types models according to the
velocity function definition
• 1st order: goal orientation and speed
density relation
• 2nd order: momentum balance equation
• Considerations
• All pedestrians share the same goals
and attitude
• Difficult to consider dynamic aspects in
the environment and situation
• Not able to capture all aspects of crowd
dynamics (e.g. some emerging
phenomena)
• Suitable for optimisation problems in
specific contexts

31. Example application and results of a recent
macroscopic approach
x−axis (m)
y−axis(m)
Densities at t = 50.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
20
25
30
35
40
45
50
0
0.5
1
1.5
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2.5
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4.5
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x−axis (m)
y−axis(m)
Densities at t = 125.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
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25
30
35
40
45
50
0
0.5
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1.5
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Figure 2: Evacuee assignment using optimal free flow routing.
y−axis(m)
Densities at t = 25.1 s
10
15
20
25
30
35
40
45
50
1
1.5
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y−axis(m)
Densities at t = 125.1 s
10
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30
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1.5
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x−axis (m)
y−axis(m)
Densities at t = 50.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
20
25
30
35
40
45
50
0
0.5
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1.5
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x−axis (m)
y−axis(m)
Densities at t = 125.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
20
25
30
35
40
45
50
0
0.5
1
1.5
2
2.5
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3.5
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4.5
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Figure 2: Evacuee assignment using optimal free flow routing.
x−axis (m)
y−axis(m)
Densities at t = 25.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
20
25
30
35
40
45
50
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
x−axis (m)
y−axis(m)
Densities at t = 125.1 s
5 10 15 20 25 30 35 40 45 50
5
10
15
20
25
30
35
40
45
50
0
0.5
1
1.5
2
2.5
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3.5
4
4.5
5
Figure 3: Evacuee assignment (iteration 30) for two time stamps. [Hoogendoorn et al., 2014]

32. Agent Based Models for simulation:
peculiarities, advantages, risks
7

33. Agent Based Models for simulation:
peculiarities, advantages, risks
7

34. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
7

35. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
7

36. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
• This means, on one hand, that
additional insight on the
modelled system is required
7

37. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
• This means, on one hand, that
additional insight on the
modelled system is required
• On the other hand such a model
should be able to
7

38. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
• This means, on one hand, that
additional insight on the
modelled system is required
• On the other hand such a model
should be able to
• Generate the same
aggregate dynamics as
traditional ones
7

39. Agent Based Models for simulation:
peculiarities, advantages, risks
• The analytical unit is the
individual agent, not aggregate
variables
• This means, on one hand, that
additional insight on the
modelled system is required
• On the other hand such a model
should be able to
• Generate the same
aggregate dynamics as
traditional ones
• Be able to represent, manage,
analyse additional aspects,
such as for instance spatial
ones
7

40. Alternative microscopic
pedestrian and crowd models
• Particle based
• Mostly, but not exclusively, social force
model and derivatives
• Continuous space and time
• Cellular Automata
• Ad-hoc rules (e.g. Blue & Adler,
intersections) or floor field approach
(e.g. Nishinari, Schadschneider, ...)
• Discrete in time and space
• Multi-Agent Systems
• Several approaches from computer
graphics (e.g. Thalmann, Terzopoulos,
Donikian, Manocha), some approaches
are extensions of CA, ...
• Behavioural models generally more
complex that in CA approaches

41. Particle based approach

42. Particle based approach
• Pedestrians à particles
subject to forces

43. Particle based approach
• Pedestrians à particles
subject to forces
• Goals: forces of attraction
generated by points/
reference point in the space
Lane formation

44. Particle based approach
• Pedestrians à particles
subject to forces
• Goals: forces of attraction
generated by points/
reference point in the space
Lane formation

45. Particle based approach
• Pedestrians à particles
subject to forces
• Goals: forces of attraction
generated by points/
reference point in the space
Lane formation
‘Freezing by heating’

46. Particle based approach
• Pedestrians à particles
subject to forces
• Goals: forces of attraction
generated by points/
reference point in the space
• Interaction among
pedestrians: forces
generated by particles
Lane formation
‘Freezing by heating’

47. Particle based approach
• Pedestrians à 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
Lane formation
‘Freezing by heating’

48. Particle based approach
• Pedestrians à 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 à tendency to stay
at a distance
Lane formation
‘Freezing by heating’

49. Particle based approach
• Pedestrians à 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 à tendency to stay
at a distance
• Attractive à imitative
mechanisms
Lane formation
‘Freezing by heating’

50. Cellular Automata and crowd modelling

51. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells

52. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)

53. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)
• Movement à generated thanks to the
transition rule

54. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)
• Movement à generated thanks to the
transition rule
– an occupied cell becomes empty and an
adjacent one, which was previously
vacant, becomes occupied

55. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)
• Movement à 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 which is
not provided by basic CAs

56. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)
• Movement à 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 which is
not provided by basic CAs
– Benefit-Cost/Gradient: predefined
information related to cell desirability

57. Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of
cells
• Pedestrian à specific state of a cell
(e.g. occupied, empty)
• Movement à 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 which is
not provided by basic CAs
– Benefit-Cost/Gradient: predefined
information related to cell desirability
– Magnetic Force/Social Force: model
the effect of presence of other agents
in the environment (attraction/
repulsion of crowds)

58. From CA to Situated MAS

59. From CA to Situated MAS
– Entities are reified, separated from the
environment

60. From CA to Situated MAS
– Entities are reified, separated from the
environment
– Agents, not just cell states

61. From CA to Situated MAS
– Entities are reified, separated from the
environment
– Agents, not just cell states
– They may have different behaviours
– Possibility to grant agents different
information about the environment
– Possibility to integrate several
different action deliberation models

62. From CA to Situated MAS
– Entities are reified, separated from the
environment
– Agents, not just cell states
– They may have different behaviours
– Possibility to grant agents different
information about the environment
– Possibility to integrate several
different action deliberation models
– Possibly heterogeneous system

63. From CA to Situated MAS
– Entities are reified, separated from the
environment
– Agents, not just cell states
– They may have different behaviours
– Possibility to grant agents different
information about the environment
– Possibility to integrate several
different action deliberation models
– Possibly heterogeneous system
– Entities interact by means of
mechanisms not necessarily related to
underlying cell’s adjacency

64. From CA to Situated MAS
– Entities are reified, separated from the
environment
– Agents, not just cell states
– They may have different behaviours
– Possibility to grant agents different
information about the environment
– Possibility to integrate several
different action deliberation models
– Possibly heterogeneous system
– Entities interact by means of
mechanisms not necessarily related to
underlying cell’s adjacency
– Action at a distance is allowed

65. Situated MAS:
action and interaction

66. Situated MAS:
action and interaction
– Agents are situated

67. Situated MAS:
action and interaction
– Agents are situated
– they perceive their context and situation

68. Situated MAS:
action and interaction
– Agents are situated
– they perceive their context and situation
– their behaviour is based on their local
point of view

69. 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 act (move) and
interact is influenced by the
environment

70. 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 act (move) and
interact is influenced by the
environment
– Situated Agents Interaction models

71. 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 act (move) and
interact is influenced by the
environment
– Situated Agents Interaction models
– Often inspired by biological systems
(e.g. pheromones, computational fields)

72. 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 act (move) and
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

73. 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 act (move) and
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
– But may also provide a direct
communication (as for CAs interaction
among neighbouring cells)

74. Groups in the literature -
Modeling and Simulation
• Extensions to the social force model
• Helbing, Theraulaz et al. 2009, 2010
• Small groups (2,3,4), unstructured
• Low to moderate densities
• Validation based on actual observations
• Xu and Duh, 2010
• Only couples (groups of 2 pedestrians)
• Low to moderate densities
• Shallow validation based on literature
(Daamen, 2004)
• CA models
• Sarmady, Haron, Zawawi Hj, 2009
• Leaders and followers
• Groups of 2 to 6 members experimented
• Not validated
• Agent-based models
• Qiu and Hu 2010
• Structured groups (intra and inter group
matrices)
• Large groups experimented (60 pedestrians)
• Not validated
• Group members tend to stay close to other group
members (additional behavioural component)

75. Meso level approaches: the best of both worlds?

76. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation
simulation projects
• Conclusions and discussion

77. Why not use simple queues?

78. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)

79. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)
Exit
(one pedestrian
every t ms)

80. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
• ... but the model would not be able
to ‘answer’ some of the questions
we can pose to the previous models
• What if t is unknown?
• Only aggregate quantities are
managed
• No heterogeneity
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)
Exit
(one pedestrian
every t ms)

81. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
• ... but the model would not be able
to ‘answer’ some of the questions
we can pose to the previous models
• What if t is unknown?
• Only aggregate quantities are
managed
• No heterogeneity
• Moreover, it would be very difficult to
manage more complex situations,
in terms of:
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)
Exit
(one pedestrian
every t ms)

82. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
• ... but the model would not be able
to ‘answer’ some of the questions
we can pose to the previous models
• What if t is unknown?
• Only aggregate quantities are
managed
• No heterogeneity
• Moreover, it would be very difficult to
manage more complex situations,
in terms of:
• Environmental structure
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)
Exit
(one pedestrian
every t ms)

83. Why not use simple queues?
• One could model such a system
with one (or more) simple queues...
• ... but the model would not be able
to ‘answer’ some of the questions
we can pose to the previous models
• What if t is unknown?
• Only aggregate quantities are
managed
• No heterogeneity
• Moreover, it would be very difficult to
manage more complex situations,
in terms of:
• Environmental structure
• Possible behaviours for the
pedestrians
Pedestrian
in the lecture
hall
Exit
(one pedestrian
every t ms)
Exit
(one pedestrian
every t ms)

84. So, no “best” modeling approach?

85. So, no “best” modeling approach?
• The choice of the abstract/
computational model depends on
several factors:

86. So, no “best” modeling approach?
• The choice of the abstract/
computational model depends on
several factors:
• Available knowledge on the
simulated phenomenon/
situation/reality

87. So, no “best” modeling approach?
• The choice of the abstract/
computational model depends on
several factors:
• Available knowledge on the
simulated phenomenon/
situation/reality
• Available data on actual
scenarios, for sake of
calibration, verification and
validation

88. So, no “best” modeling approach?
• The choice of the abstract/
computational model depends on
several factors:
• Available knowledge on the
simulated phenomenon/
situation/reality
• Available data on actual
scenarios, for sake of
calibration, verification and
validation
• Goals of the simulation activity

89. So, no “best” modeling approach?
• The choice of the abstract/
computational model depends on
several factors:
• Available knowledge on the
simulated phenomenon/
situation/reality
• Available data on actual
scenarios, for sake of
calibration, verification and
validation
• Goals of the simulation activity
• Possible tension between these
elements!

90. What about Agent Based Modeling and
Simulation methodologies?

91. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful

92. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful
• General methodologies tend to define
well understood macro phases, but no
useful suggestion on how actually carry
them out in a specific context

93. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful
• General methodologies tend to define
well understood macro phases, but no
useful suggestion on how actually carry
them out in a specific context
• For instance, in different domains it is
not even clear what is the most proper
modelling granularity level

94. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful
• General methodologies tend to define
well understood macro phases, but no
useful suggestion on how actually carry
them out in a specific context
• For instance, in different domains it is
not even clear what is the most proper
modelling granularity level
– In crowd modelling, a pedestrian is
modelled as an agent (at least in
microscopic models)…

95. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful
• General methodologies tend to define
well understood macro phases, but no
useful suggestion on how actually carry
them out in a specific context
• For instance, in different domains it is
not even clear what is the most proper
modelling granularity level
– In crowd modelling, a pedestrian is
modelled as an agent (at least in
microscopic models)…
– But in biological systems, what should be
represented as an agent? An organism?
An organ? A cell? A molecule?

96. What about Agent Based Modeling and
Simulation methodologies?
• It is very difficult to define a methodology
that is both general and actually useful
• General methodologies tend to define
well understood macro phases, but no
useful suggestion on how actually carry
them out in a specific context
• For instance, in different domains it is
not even clear what is the most proper
modelling granularity level
– In crowd modelling, a pedestrian is
modelled as an agent (at least in
microscopic models)…
– But in biological systems, what should be
represented as an agent? An organism?
An organ? A cell? A molecule?
• Specific useful (more or less) formalised
modelling approaches and
methodologies only in specific contexts
?

97. A reflection: from reality, to models, to a simulation
system
• The overall simulation project involves
several phase, roles, types of
knowledge and competences
• The frequent passages (translation,
encoding, decoding, interpretation...)
between different levels of abstraction
can lead to several problems
• Non documented assumptions
• Unrealistic/unfeasible simplifications
• Simulation projects are difficult
• R. Shannon, “Introduction to the Art and
Science of Simulation” (1998)
• ... But some even talk of “dark arts” [J.P.
Marney and H. Tarbert, “Why do
simulation? Towards a working
epistemology for practitioners of the dark
arts” (2000)]
Reality
Subsystem
Abstract model
Computational model
Simulator

98. Conclusions and
discussion
• The study of pedestrians and crowd behaviour
fruitfully comprises activity of analysis and
synthesis
• The behaviour of pedestrians and crowds can
be analysed at different levels
• Both macro and microscopic approaches to the
modelling of these phenomena are possible…
• … both have merits and limits in their
applicability
• Microscopic approaches take a very different
perspective on pedestrians and their
environment
• The contribution of different disciplines, experts
is crucial but the overall simulation project
requires particular attention
• Of course, these model, studies and application
domain can be a source for different research
area…
• … a source of new problems and potential
opportunities
• … a source of suggestions, potentially
interesting approaches/solutions

99. Outline
• Complex Systems?
• Pedestrian and crowd simulation, a brief introduction and
motivations
• Levels of analysis of pedestrian/crowd behaviour
• Approaches to pedestrian modelling (macro vs. micro)
• Macroscopic modelling rationale
• Microscopic modelling alternatives and styles
• A reflection on the specificities of crowd simulation simulation
projects
• Conclusions and discussion

100. ありがとうございます。 Giuseppe Vizzari

101. Minimal bibliography
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Schreckenberg & S. Sharma, (eds.), Pedestrian and Evacuation Dynamics, Springer, Berlin, 123–154.
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Facebook group
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2014
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