Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)
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Transcript of Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)
Simulation of complex systems: the case of crowds
Giuseppe Vizzari
Complex Systems and Artificial Intelligence Research Center (CSAI)University of Milano-Bicocca, Italy
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
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
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
Non linearity?
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)!
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
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
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
• …
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
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?
Not just “physical” crowds
Simulation: a definition and motivations
Simulation: a definition and motivations• (Computer) Simulation represents a
way to exploit a computational model
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
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
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
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
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
Simulation life-cycle
5
Simulation life-cycle
• From the target system to its computational model and a simulator
Modelingand design
of a simulator
Target System
Model and simulator
5
Simulation life-cycle
• From the target system to its computational model and a simulator• Execution of a simulation campaign
Simulation execution
Modelingand design
of a simulator
Target System
Model and simulator Data generated by the simulation(s)
5
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
Modelingand design
of a simulator
Analysis of results + interpretation
(model evaluation leading to explanation
orprediction)
Target System
Model and simulator Data generated by the simulation(s)
Collected Data
5
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
Modelingand design
of a simulator
Analysis of results + interpretation
(model evaluation leading to explanation
orprediction)
Target System
Model and simulator Data generated by the simulation(s)
Collected Data
5
A comprehensive framework for integrated synthesis and analysis
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
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 hisbehaviour. 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 eachlevel are indicated in this figure.
STRATEGIC
TACTICAL
OPERATIONAL
Activity set choice
Derivedactivity set
Network topology
Timetable
RouteActivity schedule
Activity areas
Route choice
Activity area choice
Activity scheduling
Dynamic networkcharacteristics
Geometry
ObstaclesVehicle characteristics
WalkingWaiting
Performing an activityTrajectory choice
Figure 5.1: Levels in pedestrian behaviour based on Hoogendoorn et al. (2001) and figure 3.1
At the strategic level, travellers decide on the activities to be performed in the station.While some of these activities may be discretionary (e.g. buying a newspaper), othersmay be mandatory (validating a ticket before accessing a train). The activity choice setmay be related to characteristics of the pedestrian as well as of the infrastructure (avail-ability, type and location of shops). At the time of writing, the individual’s activity choiceset, including the fact whether activities are mandatory or not, is assumed exogenous inSimPed, given in the form of a distribution of various pedestrian types over the activities.The activity choice set contains the maximum number of activities each pedestrian willperform. All other choices at the strategic level, such as mode choice and departure timechoice are also exogenous and form input for the simulation tool.
The tactical level pertains to short-term decisions of the pedestrian in the facility, giventhe choices made at the strategic level. On the strategic level, pedestrians determine a listof activities they want to perform in the transfer node or pedestrian facility. The activitieson this list may be performed at different locations. Optimal locations for an individual
[Hoogendoorn et al., 2001]
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
Modeling approaches: what to model?
Pedestrian/Crowdmodels
Macroscopic(continuous fluids)
Microscopic(individual pedestrians)
Overall equations (mass conservation,flow velocity) determining velocity anddensity in the analysed area
Individual state, position, decision aremodelled; aggregate measures derivefrom these decisions
+ lower computational costs for large crowd- limited applicability
+ generally greater applicability- higher computational costs for large crowd
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 classes1st order models: velocity given by a phenomenologicalspeed-density relation ~v = V (⇢)~⌫
2nd order models: velocity given by a momentum balance equation
Density must stay non-negative and bounded: 0 ⇢(t,x) ⇢
max
Different from fluid dynamics:preferred directionno conservation of momentum / energyn ⌧ 6 · 1023
1R.L. Hughes, Transp. Res. B, 2002P. Goatin (INRIA) Mathematical modeling of crowds January 12, 2014 5 / 22
Mathematical models Numerical tests Conclusion
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, 2014 7 / 22
[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
Example application and results of a recent macroscopic approach
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Figure 2: Evacuee assignment using optimal free flow routing.
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Densities at t = 25.1 s
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Figure 3: Evacuee assignment (iteration 30) for two time stamps.
12
TRB 2014 Annual Meeting Paper revised from original submittal.
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xis
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Densities at t = 50.1 s
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Figure 2: Evacuee assignment using optimal free flow routing.
x−axis (m)
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xis
(m)
Densities at t = 25.1 s
5 10 15 20 25 30 35 40 45 50
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Figure 3: Evacuee assignment (iteration 30) for two time stamps.
12
TRB 2014 Annual Meeting Paper revised from original submittal.
[Hoogendoorn et al., 2014]
Agent Based Models for simulation: peculiarities, advantages, risks
7
Agent Based Models for simulation: peculiarities, advantages, risks
7
Agent Based Models for simulation: peculiarities, advantages, risks
• The analytical unit is the individual agent, not aggregate variables
7
Agent Based Models for simulation: peculiarities, advantages, risks
• The analytical unit is the individual agent, not aggregate variables
7
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
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
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
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
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
Particle based approach
Particle based approach
• Pedestrians à particles subject to forces
Particle based approach
• Pedestrians à particles subject to forces
• Goals: forces of attraction generated by points/reference point in the space
Lane formation
Particle based approach
• Pedestrians à particles subject to forces
• Goals: forces of attraction generated by points/reference point in the space
Lane formation
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’
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’
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’
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’
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’
Cellular Automata and crowd modelling
Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of cells
Cellular Automata and crowd modelling
• Environment à bi-dimensional lattice of cells
• Pedestrian à specific state of a cell (e.g. occupied, empty)
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
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
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
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
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)
From CA to Situated MAS
From CA to Situated MAS
– Entities are reified, separated from the environment
From CA to Situated MAS
– Entities are reified, separated from the environment– Agents, not just cell states
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
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
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
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
Situated MAS:action and interaction
Situated MAS:action and interaction– Agents are situated
Situated MAS:action and interaction– Agents are situated
– they perceive their context and situation
Situated MAS:action and interaction– Agents are situated
– they perceive their context and situation– their behaviour is based on their local
point of view
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 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
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)
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
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)
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)
Meso level approaches: the best of both worlds?
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
Why not use simple queues?
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)
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)
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)
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)
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)
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)
So, no “best” modeling approach?
So, no “best” modeling approach?• The choice of the abstract/
computational model depends on several factors:
So, no “best” modeling approach?• The choice of the abstract/
computational model depends on several factors:• Available knowledge on the
simulated phenomenon/ situation/reality
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
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
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!
What about Agent Based Modeling and Simulation methodologies?
What about Agent Based Modeling and Simulation methodologies?• It is very difficult to define a methodology
that is both general and actually useful
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
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
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)…
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?
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
?
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
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
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
ありがとうございます。 Giuseppe Vizzari
Minimal bibliography
• Stefania Bandini, Andrea Gorrini, Giuseppe Vizzari: Towards an integrated approach to crowd analysis and crowd synthesis: A case study and first results. Pattern Recognition Letters 44: 16-29 (2014).
• Hoogendoorn, S.P., P.H.L. Bovy & W. Daamen (2001), Microscopic pedestrian wayfinding and dynamics modelling, In: M. Schreckenberg & S. Sharma, (eds.), Pedestrian and Evacuation Dynamics, Springer, Berlin, 123–154.
• Andreas Schadschneider, Wolfram Klingsch, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried: Evacuation Dynamics: Empirical Results, Modeling and Applications. Encyclopedia of Complexity and Systems Science 2009: 3142-3176.
• Paola Goatin (2014), Mathematical modeling of crowds. Presentation at TRB 2014 Workshop “Crowd Flow Dynamics, Modeling and Management” - available in “TRB Subcommittee on Crowd Flow Dynamics, Modeling, and Management” Facebook group
• Serge Hoogendoorn, Winnie Daamen, Dorine Duives, and Femke van Wageningen-Kessels: Optimal Crowd Evacuation. TRB 2014
• Helbing, D., A. Johansson, and H. Z. Al-Abideen, Dynamics of crowd disasters: An empirical study. Phys. Rev. E, Vol. 75, 2007, p. 046109.
• Colombo, Rinaldo M., Paola Goatin, Giulio Maternini, and Massimiliano D. Rosini. "Macroscopic Models for Pedestrian Flows." In Big events and transport: the transportation requirements for the management of large scale events. 2010.
• Daamen, Winnie, Serge P. Hoogendoorn, and Piet HL Bovy. "First-order pedestrian traffic flow theory." Transportation Research Record: Journal of the Transportation Research Board 1934, no. 1 (2005): 43-52.
• Helbing, Dirk, and Peter Molnar. "Social force model for pedestrian dynamics." Physical review E 51, no. 5 (1995): 4282.• Klüpfel, H., A Cellular Automaton Model for Crowd Movement and Egress Simulation. Ph.D. thesis, University Duisburg-Essen,
2003.• Burstedde, C., K. Klauck, A. Schadschneider, and J. Zittartz, Simulation of pedestrian dynamics using a two-dimensional
cellular automaton. Physica A: Statistical Mechanics and its Applications, Vol. 295, No. 3 - 4, 2001, pp. 507 – 525.• Vizzari, G., L. Manenti, and L. Crociani, Adaptive Pedestrian Behaviour for the Preservation of Group Cohesion. Complex
Adaptive Systems Modeling, Vol. 1, No. 7, 2013.