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Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)

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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"

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Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)

  1. 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. 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. 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. 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. 5. Non linearity?
  6. 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. 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. 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. 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. 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. 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. 12. Not just “physical” crowds
  13. 13. Simulation: a definition and motivations
  14. 14. Simulation: a definition and motivations • (Computer) Simulation represents a way to exploit a computational model
  15. 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. 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. 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. 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. 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. 20. Simulation life-cycle 5
  21. 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. 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. 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. 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. 25. A comprehensive framework for integrated synthesis and analysis
  26. 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. 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. 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. 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. 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. 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 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 3 3.5 4 4.5 5 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 2 2.5 3 3.5 4 4.5 5 y−axis(m) Densities at t = 125.1 s 10 15 20 25 30 35 40 45 50 1 1.5 2 2.5 3 3.5 4 4.5 5 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 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 3 3.5 4 4.5 5 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 3 3.5 4 4.5 5 Figure 3: Evacuee assignment (iteration 30) for two time stamps. [Hoogendoorn et al., 2014]
  32. 32. Agent Based Models for simulation: peculiarities, advantages, risks 7
  33. 33. Agent Based Models for simulation: peculiarities, advantages, risks 7
  34. 34. Agent Based Models for simulation: peculiarities, advantages, risks • The analytical unit is the individual agent, not aggregate variables 7
  35. 35. Agent Based Models for simulation: peculiarities, advantages, risks • The analytical unit is the individual agent, not aggregate variables 7
  36. 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. 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. 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. 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. 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. 41. Particle based approach
  42. 42. Particle based approach • Pedestrians à particles subject to forces
  43. 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. 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. 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. 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. 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. 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. 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. 50. Cellular Automata and crowd modelling
  51. 51. Cellular Automata and crowd modelling • Environment à bi-dimensional lattice of cells
  52. 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. 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. 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. 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. 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. 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. 58. From CA to Situated MAS
  59. 59. From CA to Situated MAS – Entities are reified, separated from the environment
  60. 60. From CA to Situated MAS – Entities are reified, separated from the environment – Agents, not just cell states
  61. 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. 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. 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. 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. 65. Situated MAS:
 action and interaction
  66. 66. Situated MAS:
 action and interaction – Agents are situated
  67. 67. Situated MAS:
 action and interaction – Agents are situated – they perceive their context and situation
  68. 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. 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. 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. 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. 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. 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. 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. 75. Meso level approaches: the best of both worlds?
  76. 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. 77. Why not use simple queues?
  78. 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. 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. 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. 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. 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. 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. 84. So, no “best” modeling approach?
  85. 85. So, no “best” modeling approach? • The choice of the abstract/ computational model depends on several factors:
  86. 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. 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. 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. 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. 90. What about Agent Based Modeling and Simulation methodologies?
  91. 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. 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. 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. 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. 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. 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. 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. 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. 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. 100. ありがとうございます。 Giuseppe Vizzari
  101. 101. 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.

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