Presentation held at the "Crowd Flow Dynamics, Modeling, and Management" workshop, in the context of the 93rd Annual Meeting of the Transportation Research Board, Washington DC - Jan. 12, 2014

1. Adaptive pedestrian behaviour for the
preservation of group cohesion: observations
and simulations
Giuseppe Vizzari
!
Complex Systems and Artificial Intelligence Research Center (CSAI)
University of Milano-Bicocca, Italy
!

2. Outline
• Pedestrian and crowd simulation: why groups?
• Some observations and empirical data
• An adaptive pedestrian model for preserving group cohesion
• The model in a benchmark scenarios
• Conclusions and discussion
TRB 2014 - Washington DC - Jan. 12, 2014

3. Impact of groups in pedestrian
and crowd dynamics
• Most modelling 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?
TRB 2014 - Washington DC - Jan. 12, 2014

4. Groups in the literature
- Observations
• At least two studies report observations
about groups
• Willis A, Gjersoe N, Havard C,
Kerridge J, Kukla R, 2004, "Human
movement behaviour in urban spaces:
implications for the design and
modelling of effective pedestrian
environments" Environment and
Planning B: Planning and Design 31(6)
805 – 828
• Michael Schultz, Christian Schulz, and
Hartmut Fricke. “Passenger Dynamics
at Airport Terminal Environment”,
Pedestrian and Evacuation Dynamics
2008, Springer-Verlag, 2010
• Observations carried out in low density
conditions
• Groups of small size were most frequently
observed
TRB 2014 - Washington DC - Jan. 12, 2014

5. 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)
TRB 2014 - Washington DC - Jan. 12, 2014

6. Admission test
University of Milano-Bicocca
• Admission test of the Faculty of
Psychology at the University of MilanoBicocca - September 1, 2011
• Counting activity supported by video
footages of the event
• About two thousand students attended
the test
• About 34% individuals, 50%
couples, 13% triples and 3%
groups of 4 members (!)
• Statistically validated relationship
between group size and velocity
• Additional quantitative analyses about
the arrival and entrance process, LOS
• Qualitative analysis of group shapes and
related phenomena
• More details in an ACRI 2012 (C&CA) and
upcoming PED 2012 paper
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7. Vittorio Emanuele II
Gallery, Milan
• Popular commercial-touristic
walkway in Milan’s city centre
• Goals of the survey:
• level of density and walkway
level of service (A and B);
• presence of groups (over 84%);
• group size and proxemics
spatial patterns, trajectories and
walking speed (groups are
slower but their trajectories
are shorter);
• group proxemics dispersion
(they preserve cohesion, even
if large ones occupy more
space)
• still hard to evaluate spatial
arrangement of group members
TRB 2014 - Washington DC - Jan. 12, 2014
Group
dispersion
Couples
Triples
4 Members
Distance
Centroid
0.58 m
(sd 0,22)
0.76 m
(sd 0,11)
0.67
(sd 0.12)

8. A model considering
groups
• Based on the floor-field CA approach,
with significant difference on
movement choice
• Employing traditional factors for
movement destination choice
• Goal orientation
• Presence of obstacles
• Presence of other pedestrians
(basic proxemics)
• A notion of group has been introduced
• To generate a generalised effect of
cohesion among members of
groups
• ... able to overcome goal
orientation for certain types of
groups (e.g. families, close friends)
• Speed heterogeneity also introduced
(poster on Monday afternoon)
TRB 2014 - Washington DC - Jan. 12, 2014
Considered factors:
+ cell is closer to pedestrian's goal
(voided by high group dispersion)
+ presence of group members nearby
- presence of other pedestrians nearby
- presence of obstacles nearby
Movement blocking factors:
- cell is occupied by another pedestrian
(but in very high densities the cell can
be shared by two pedestrians)
- cell is occupied by an obstacle

9. A few formal details
• Stochastic choice of destination cell; for each cell c, the probability of
choosing an action a leading to it is
• The “utility” value of the cell is defines as follows:
where
Goal is associated to the static floor field and Obs to the wall potential
Sep is associated to the proxemic repulsion
D is an inertia factor
Over regulates the possibility of having two pedestrians sharing the same cell in
case of high density
• Coh and Inter represent group cohesion factors respectively for small simple
groups and large potentially structured groups
•
•
•
•
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10. Overlapping
• Overlapping is a transient
situation in which pedestrians
share the same cell
• ... it can sometimes be
observed in counterflow
situations in which there is
not enough space for
avoidance
• It can only happen if local
density exceeds a given
threshold
• The choice is still penalised
(Over ≤ 0)
• No more than two pedestrians
can share a single cell
TRB 2014 - Washington DC - Jan. 12, 2014
[Kretz et al., 2006]

11. Simple and
structured groups
• Simple groups are made up of family
members, friends, people that know
each other
• They often adapt their behaviour to
preserve the cohesion of the group
(b) B
• Large groups can include perfect
strangers that share for some time a
common goal
• Members of this group have a
tendency to stay close to each
other...
• ... but this tendency is not so strong
to prevent group fragmentation
• And generally they are actually
structured (they can include other often simple - groups), so we call
them structured
(c) U
Figure 4. Snapshots from the expe
TRB 2014 - Washington DC - Jan. 12, 2014

12. [12]
Recent works represent a relevant effort towards the modeling of groups,
respectively in particle-based [9, 18] and in agent-based approaches [11]: in
all these approaches, groups are modeled by means of additional contributions to the overall pedestrian behaviour representing the tendency to stay
close to other group members. However, most of the above approaches only
deal with small groups in relatively low density conditions or they were not
ofvalidated against real data.
the different components of movement “utility” are
Adaptive group cohesion mechanism
• Multipliers
according to the state of the group
!
Balance(k) =
!
!
8
> 1 · k + ( 2 · k · DispBalance)
>3
3
<
1
>3
>
:k
· k + ( 2 · k · (1
3
adjusted
if k = kc
DispBalance)) if k = kg _ k = ki
otherwise
4.2 Finer Scale of Discretization
[8]
[13]
• The dispersion of the group causes an increased impact of simple group
4.3 Different Types of Pedestrians
cohesion and a reduced effect of goal attraction (static floor field)
[8]
[17]
4.4 Conclusions and Future Developments
ACKNOWLEDGMENTS
REFERENCES
[1] Victor J. Blue and Jeffrey L. Adler. (2000). Modeling four-directional pedestrian flows.
Transportation Research Record, 1710:20–27.
10
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13. Modelling groups - some qualitative results
Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor
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14. Aggregate effects of groups
Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor;
shuffled sequential update - ongoing tests with parallel update strategy
TRB 2014 - Washington DC - Jan. 12, 2014

15. Aggregate effects of
groups analysed
• We can interpret the results making
considering two phenomena
1.Wide groups offer a large profile to
the counter flow, so they have a higher
probability of facing conflicts
2.Once a group has formed a line,
instead, the leader has the same
conflict probability of an individual, but
the follower has often an advantage
• In low density situations phenomenon (1)
prevails, leading to a lower average
combined flow for groups of pedestrians
whose size is larger than 2
• Pairs in fact can easily form a line,
turning phenomenon (1) to (2)
• In high density situations the probability of
facing conflicts is very high also for
individuals, so phenomenon (2) prevails,
leading to higher average combined flow
for even large groups (size 5)
TRB 2014 - Washington DC - Jan. 12, 2014

16. Effectiveness of simple group cohesion mechanism
Counterflow of two structured groups including simple groups of various size, in a 3.6 m wide corridor
(Dispersion measured in terms of area covered by the group)
TRB 2014 - Washington DC - Jan. 12, 2014

17. Additional results in “experimental” scenarios: T
junction
4
J Zhang, W Klingsch, A Schadschneider, and A Seyfried
5
T-240-100-240-right
T-240-100-240-left
4
3
y [m]
2
1
0
-1
-2
-3
-6
-4
-2
0
2
4
x [m]
(a) Snapshot
(b) Pedestrian trajectories
Fig. 2. Trajectories and snapshot from T-junction experiment.
From these trajectories, pedestrian characteristics including flow, density, velocity and individual distances at any time and position can be determined.
3 Experiment analysis
Plot of experimentally observed data
[Zhang et al., 2012]
TRB 2014 - Washington DC - Jan. 12, 2014
In previous studies, different measurement methods were used to limit the
comparability and fluctuation of the data. E.G. Helbing et al. proposed a
Gaussian, distance-dependent weight function [13] to measure the local density and local velocity. Predtechenskii and Milinskii [14] used a dimensionless
definition to consider different body sizes and Fruin introduced the ”Pedestrian Area Module” [15]. This study focuses on the Voronoi method proposed
in [16, 17], where the density distribution can be assigned to each pedestrian.
This method permits examination on scales smaller than the pedestrians for
its high spatial resolution.
[Vizzari et al., 2013]
3.1 Measurement methodology

18. Conclusions and
discussion
• Groups are relevant and significant
• Results of simulations are partly
validated
• Fundamental diagram and spatial
utilisation in tune with results from the
literature… without groups
• Group cohesion mechanism
generates results about dispersion
that are in tune with Vittorio Emanuele
Gallery’s observation…
• … but we don’t have data about
groups in high density situations (and
it’s hard to obtain such data)
• More observations, experiments and
simulations are necessary to improve
our understanding of the phenomenon
• Closer collaboration between
researchers working on synthesis and
analysis of crowds is promising and
possibly beneficial for both
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19. ありがとうございます。
Giuseppe Vizzari