A Multidisciplinary Approach to Crowd Studies

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AACIMP 2009 Summer School lecture by Sara Manzoni. "Mathematical Modelling of Social Systems" course. 3rd hour.

AACIMP 2009 Summer School lecture by Sara Manzoni. "Mathematical Modelling of Social Systems" course. 3rd hour.

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  • 1. 4th Summer School AACIMP-2009 Achievements and Applications of Contemporary Informatics, Mathematics and Physics A multidisciplinary approach to crowd studies Lecture 1 – 11.08.2009 Dr. Sara Manzoni Complex Systems and Artificial Intelligence research center Department of Computer Science, Systems and Communication University of Milano-Bicocca
  • 2. What is a Crowd? Contributions from social sciences, social psychology on Human behavior and social collectivities Some definitions • “Too many people in too little space” (Kruse, 1996) • “A gathering of individuals that influence one another and share a purpose, intent or emotional state in a limited space” [Blumer] • A crowd is a form of collective action: “two or more persons engaged in one or more actions (e.g. locomotion, orientation, vocalization, verbalization, gesticulation, and or manipulation), judged common or concerted on one or more dimensions (e.g. direction, velocity, time, or substantive content” (McPhail, 1991)
  • 3. What is a Crowd? Contributions from social sciences, social psychology on Human behavior and social collectivities Theories • Contagion/Transformation Theory Gabriel (Tarde; Le Bon; Blumer, Canetti) Tarde Gustave LeBon • Convergence Theory Elias Canetti (Berk, Floyd Allport, Neal Miller, John Dollard) • Emergent Norm Theory Robert Ezra Park Herbert Blumer (Turner, Killian) • Value Added Theory (Smelser) Neil Smelser
  • 4. Traditional Theories of Crowd Behavior Contagion T heory  Crowd behaviour is irrational The crowd • exert an effect on its members • forces individuals to action thanks to anonymity that encourages people to abandon rationality and responsibility • helps emotion propagation that can drive to irrational and suitably violent action
  • 5. Traditional Theories of Crowd Behavior Convergence theory  Crowd behavior is rational Crowd behavior is • not the result of the crowd itself • carried inside the crowd by specific individuals • People that would like to behave and act in a certain way come together in order to form and constitute a crowd • Crowd behavior expresses values and beliefs that are already present in the population (i.e. racist feelings) • The mob is a rational product of rational values
  • 6. Traditional Theories of Crowd Behavior • E m ergent/ nor m theory  Crowd behavior is not fully predictable but it is not irrational • People in a crowd assume different roles (e.g. some participants become leaders, other lieutenants, followers, inactive bystanders or even opponents) • Common interests can bring people together in a crowd, but different patterns (of behavior) can emerge inside the crowd itself • Norms inside a crowd can be vague and changing in the process of aggregation (people state their own rules while participating at the crowd) • Decision-making has a preponderant role in the behavior of the crowd although external observers may find it difficult to realize
  • 7. “ the reason why good data on crowd and collective behavior are so scarce is that data are function of theorethical guidance and Herbert Blumer 1900-1987 existing theories provide no guidance; but, useful theories cannot be built in the absence of empirical data ...” Freely taken from:Clark McPhail, Blumer’s theory of collective behavior: The development of a Non-symbolic interaction explanation, The Sociological Quaterly, Volume 30, Number 3, JAI press 1989
  • 8. Crowd study: Contributions/Open issues from computer science Better comprehension of crowd phenomena (crowd study) and development of tools (for crowd study and crowd management) – Data acquisition techniques and technologies Data acquisition techniques and technologies • Direct observation (Stalking, Questionnaires) • Scene analysis • Proximity detection (RF-ID) • Localization systems (GPS, sensor networks) – Crowdmodeling andand simulation Crowd modeling simulation • Modeling, computational, analysis tools • Simulation and visualization tools – Knowledge representation • Data representation and analysis M ultidisciplina • Experts’ knowledge ry F ield of • Available theory/results from social sciences S tudy
  • 9. Data Acquisition HOW CAN CROWDS AND INDIVIDUALS BE MEASURED? WHAT CAN BE MEASURED? HOW CAN AVAILABLE TECHNOLOGIES MEASURE CROWDS? S. Bandini, M.L. Federici, S. Manzoni “A qualitative evaluation of Technologies and Techniques for Data Acquisition on Pedestrians and Crowded” Proc. of Special session “At man’s step”@SCSC07, San Diego, CA
  • 10. What are we interested in Measuring: Data on the crowd • Number of people (static or inside a march) • Density of the crowd • Flow, Pressure and times of ingress/egress from a place • Groups movement inside a crowd • …
  • 11. What are we interested in Measuring: Data on individuals • Trajectories in a specific environment • Walking speed in different situations • Physical Behavior: – Queuing – Streaming – Group formation – Separation – Cohesion – Imitation – …
  • 12. Measuring Crowds: HOW? Mature and emergent technologies for data acquisition Stalking (following people • Direct Observation, interviews, without being seen!) questionnaires, stalking • Technologies for people positioning and counting – Scene analysis: TV Camera – Global Positioning System (GPS) – Proximity technologies (Radio Frequency IDentification – RFID) – Sensor Networks – PDAs, SmartPhones (GPRS, Wi- Fi) – Dead reckoning (portable inertial platform)
  • 13. Data acquisition: an example (2005) Application of GIS/GPS to track pedestrian movements – Position, velocity, trajectories – Critical areas identification N. Koshaak (Makkah - Saudi Arabia)
  • 14. Comparing Data Acquisition Technologies and Techniques (1) Scalability 5 Continuous Localization Single Individual Monitoring 4 3 2 Precise Localization Data Entire Crowd/groups Monitoring 1 0 Outdoor Indoor 0: null quality 1: insufficient 2: just sufficient 3: discrete Cheap Large Scale 4: good 5: best Available Small Scale Absolute Position System (GPS) Proximity Tech. (Passive RFiD) Scene Analysis (Video Analysis)
  • 15. Comparing Data Acquisition Technologies and Techniques (2) Scalability 5 Continuous Localization Single Individual Monitoring 4 3 2 Precise Localization Data Entire Crowd/groups Monitoring 1 0 0: null quality Outdoor Indoor 1: insufficient 2: just sufficient 3: discrete Cheap Large Scale 4: good 5: best Available Small Scale Dead Reckoning (Portable Inertial Platform) Sensor Network (ZigBee) Direct Observation (People Counting)
  • 16. San Diego (CA) At man’s Step special track at Summer Computer Simulation Conference 2007 Jul, 13-14 2007 S. Bandini, M.L. Federici, S. Manzoni, “A qualitative comparison of technologies for Data Acquisition on Pedestrians and Crowded Situations”
  • 17. Crowd modeling and HOW DO PEOPLE BEHAVE IN CROWDED SPACES AND simulation SITUATIONS? SIMULATIONS ARE EXPERIMENTAL LABORATORIES FOR HUMAN SCIENCES
  • 18. Crowd modeling and application directions • Support the study of pedestrians/crowds behavior – Envisioning of different behavioral models in realistic environments – Possibility to perform ‘in-machina’ experiments • Decision makers might not be experts neither on crowd dynamics nor on software and math tools – Need of effective ways to edit, execute, visualize and analyze simulations (what-if scenarios) • Indoor (Buildings, Shopping Centers, Stadiums) vs Outdoor (Urban spaces for public events/transport, Parades, Marches, Fairs, Sport Events, Concerts)
  • 19. Examples of Crowd Dynamics • Evacuation dynamics – normal vs panic – open/structured spaces • Lane formation and other self- organization phenomena • Crowd formation/dispersion • Crowd movement and behavior (e.g. in shopping center)
  • 20. Schema of the abstract levels involved in a simulation Phases1: observation canpassages implied in Hypothesissimulationsimulation IfPhase 2: at the to 4 of beTarget System  The the (Model),athat could Phase from 1 different the seen as of an theof Target System has to be Formulation phases Building Abstraction Phaseconcerned with models implicitly we areconstruction the Abstract we look Modelization Phase Computational or informal. can’t be separated of the next). 3:a data on it vague that and abstraction. Translation models. and aresimplyMas even be 4: we can see Modeling Software Implementation working withfrom of the observed and intuitive, collected (this phase model using Translation of many  Levels Computational Model the of Model (language/entities). This of the Model into something selective instead and an Hypothesis on what is To observe thephasesinlevel Software Code Each model represents a concernabstraction.the Decoding Levels Phase The next Computational attention How many levels of abstraction constitutes the point of “no return” observed must be already present in to reality (an interpretation key that are involved in a simulation process? the observer) simulation explains how translate back entities of the computational model in entities of reality is needed) 4) Phase 4: Software The Software Model (operational model) implementation Software 4 SW of the Computational Model 4 The computational model is what we use 3) Phase to Computational abstract model. The 3: represent the Comp. MAS 3 3 modeling computational model is always a formal Model model. It can be a model Agent Based or Cellular Automata based etc. The abstract model of the target system Abstract M 2) Phase 2: Model / Theory Construction language, can be expressed in natural 2 Model mathematical formulas etc. but usually it is 2 not computational. It can be, at first, also anThe target system is the object of study intuitive set of rules. (Physical model). It is a specific point of Target view on a portion of reality that we consider 1 TS 1) Phase 1: Observation the context. The target “isolated” from System 1 system is determined by our observation perspective on reality, and by the aspects of reality that we want to capture (i.e. atom level; molecular; macro-level) 0 Reality
  • 21. 1) Observation 5) Computation (sim running) 2) Model / Theory Construction 6) Visualization 3) Computational modeling 7) Verification of Sim in respect to the Theory 4) Software implementation 8) Validation of Theory in respect to Real Data 9) Prediction Abstraction Phase 5: 7:assumptions Simulation is Validatedthe it Decoding Output:Phase Displaying:(calculus-computation) Phase9: prediction  of The theory meaningful Theory:8:Verification  the theory ofhas then for Phasesimple results and thesoftware appropriate Simulation 6: Visualization anrunning and Software run If the envisioning of the Real Data: collection constitute to Validation rules that Levels execution thatin simulationonly to initialobservable available data then itrespect to our on the base description of is often the outputs in theory hascheckedof relation to reality makeoperate of measurements domain be to be outputs verifiedin is target system translation the in the possible way to predictions Levels for future simulation dynamic data objects states of the Target System indicators with the chosen in precedent phase 5 4 SW O 6 Output 4 Software Simulation 3 Comp. 3 MAS SD 7 Model Displaying Abstract M T 2 8 Theory Model 2 1 Target TS R Real Data System 1 9 0 Reality
  • 22. Decoding and correcting • Abstract Model Revision: If Check: a Campaign results are that gave capture Computational Model Change: Computational Model is judged obtained a deeper Software Implementation the After If no suitableof Simulationadequate tonegative the theory assumptions, but realbe needed in order to my theory in translates results a check of the assumptionsdata don’t give translate verify that itthe model is check into the software may that I made to a positive feedback, abstract model must undergo a revision that will Model can eventually on new hypothesis and needed. Athe computational model lie on the constructionbe necessary. properly change of the computational empirical observations 4 Software SW O So f Ch tware eck Imp lem Comp. MAS en t SD 3 Com atio Model put n atio nal Abstract Mod M el C 2 han T Model Abs ge trac t Mo Target del 1 TS Rev R System isio n
  • 23. Mapping of the phases of Design, Inference, Interpretation and Analysis in the Schema 4 Software SW Inference O Comp. MAS SD 3 Model Interpretation Design Abstract 2 M T Model Target Analysis 1 TS R System
  • 24. Steps in Physical Scientific Practice Experiment/ Hypothesis/ Validation Observation Theory Building Physical Prediction Model Mathematical Inference/ Model Translation in Deduction Mathematical Framework From: Modeling Games in the Newtonian World, by David Hestenes
  • 25. Comparison Between Abstraction Implied in Physic Scientific Practice and MAS Simulation Practice Abstraction in Mabs Abstractions in Physical Theories One Step More of Abstraction 4 Software SW is implied Mas Model Comp. MAS Corresponds to 3 Mat Model Mathematical Model Abstract 2 M M Model Target 1 TS TS System reality
  • 26. Pedestrian Movement at the Micro-Scale: Social Force Model [Batty, Helbing (2001)] • Four principles “guide” movement – Agents avoid obstacles present in the environment – Agents consider repulsive the presence of other pedestrians when space is congestioned – Agents also attract each other (principle of the flocking) – Agents “desire” to follow a direction • To each of these components it is associated a force that pushes the agent towards a specific direction New Position = Old Position + Desired Position + Geometric Repulsion + Social Repulsion + Social Attraction + ε
  • 27. Crowd modeling Analytical (physical) approach Lane formation • Pedestrians  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 ‘Freezing by heating’ – Repulsive  tendency to stay at a distance – Attractive  imitative mechanisms D. Helbing, I. J. Farkas, T. Vicsek: Freezing by Heating in a Driven Mesoscopic System, PHYSICAL REVIEW LETTERS, VOLUME 84, NUMBER 6, 2000
  • 28. Crowd modelling: Cellular Automata • Environment  bidimensional lattice of cells • Pedestrian  specific state of a cell (e.g. occupied, empty) • Movement  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 on – Benefit-Cost/Gradient: information about “cell desirability” – Magnetic Force: models the effect of presence of other agents in the environment (attraction/repulsion of crowds)
  • 29. Crowd modelling: From CA to Situated MAS • Individuals are separated from the environment – Agents, not just cell occupancy states – may have different behaviors: several action deliberation models can be integrated – heterogeneous system • Agents interact by means of mechanisms not necessarily related to underlying cell’s adjacency – Action at a distance is allowed
  • 30. 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 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 – May also provide a direct communication (as for CAs interaction among neighbouring cells)
  • 31. Situated Cellular Agents (SCA) Multi Agent model providing react(s,ab,s’) react(s,ac,s’) • Explicit representation of agents’ environment • Interaction model strongly related to agents’ positions in the environment – Among adjacent agents (reaction) – Among distant agents, through field emission- emit(f) diffusion-perception mechanism • Possibility to model heterogeneous agents, with different perceptive capabilities and behaviour CompareT(f×c,t) = true
  • 32. Situated Cellular Agents (SCA) • Formal and computational framework to represent and study of dynamics in pedestrian systems – autonomous interacting entities – situated in an environment whose spatial structure represents a key factor in their behaviors (i.e. actions and interactions) • Based on MMASS (Multilayered Multi-Agent Situated Systems) [S. Bandini, S. Manzoni, C. Simone, Dealing with Space in Multi- Agent System: a model for Situated MAS, in Proc. of AAMAS 2002, ACM Press, New York, 2002] • MMASS relaxes constraints on uniformity, locality and closure of CA [S. Bandini, S. Manzoni, C. Simone: Enhancing Cellular Spaces by Multilayered Multi Agent Situated Systems, Proc. of ACRI 2002: 156-167] – Open systems can be modeled – Not homogeneous agent environment – Heterogeneous agents – Interaction involving spatially not adjacent agents
  • 33. SCA model Spatial structure Agents and behaviours At-a-distance interaction
  • 34. SCA Space • Space: set P of sites arranged in a network • Each site p∈P is defined by <ap, Fp, Pp> where – ap∈A ∪{⊥}: agent situated in p – Fp⊂F: set of fields active in p – Pp⊂P: set of sites adjacent to p
  • 35. SCA Fields • <Wf, Diffusionf, Comparef, Composef> – Wf: set of field values – Diffusionf: P X Wf X P Wf X…X Wf field diffusion function – Composef: Wf X …X Wf Wf field composition function – Comparef: Wf X Wf  {True, False} field comparison function • Fields are generated by agents to interact at-a-distance and asynchronously
  • 36. SCA Agents • a∈A : <s,p,T> • T  < ∑T, PerceptionT, ActionT> – ∑T: set of states that agents can assume – ActionT: set of allowed actions for agents of type T – PerceptionT: ∑T [N X Wf1] … [N X Wf|F|] • PerceptionT(s) = (cT(s), tT(s)) • cT(s): perception modulation • tT(s): sensibility threshold • An agent a = <s,p,T> perceives the field value wfi of a field fi = <Wf, Diffusionf, >i, *i>when ciT(s)*fi wfi,>fi tiT(s)) and
  • 37. SCA Agent Actions • ActionsT: set of actions that agents of type T can perform • Agent behavior: perception-deliberation-action cycle – Perception of local environment (e.g. free sites, fields) – Action selection based on agent state, position and type – Action execution • Four basic actions – intra-agent actions: triggerT(), transportT() – inter-agent actions: emitT(), reactionT() action: trigger(s,fi,s’) action: reaction(s, ap1, ap2, …, apn,s’) condit: state(s), perceive(fi) condit: state(s), agreed(ap1, ap2,…, effect: state(s’) apn) action: transport(p,fi,q) effect: state(s’) action: emit(s,f,p) condit: position(p), empty(q), near(p,q), condit: state(s) perceive(fi) effect: present(f, p)
  • 38. Situated MAS and crowd modeling • Pedestrians  agents • Environment  graph, as an abstraction of the actual environmental structure • Movement  generated thanks to perception-action mechanism – Sources of signals: relevant objects transport(p,q) (gateways, reference points), but also other agents – Agents are sensitive to these signals and can be attracted/repelled by them (amplification/contrast) – Possible superposition of different such effects
  • 39. SCA Crowd Modelling Approach Abstract scenario Computational model for Experiment-specific specification the scenario parameters Definition of the MMASS spatial structure Definition of active Definition of monitored elements of the parameters and environment specification and field types of monitoring mechanisms Specific simulation Definition of mobile agents configuration (number, type, (types, states, perceptive position and initial state of capabilities and behavioural mobile agents, other specification) parameters)
  • 40. Underground scenario • An underground station (several interesting crowd behaviors can be studied) • Passengers' behaviors are difficult to predict: crowd dynamics emerges from single interactions – between passengers – between single passengers and parts of the environment (signals, constraints) • Passengers (actions) – on board may • have to get off • be looking for a seat or try standing beside a handle • be seated – on the station platform may • try to reach for the exit door • get on the train • Passengers have to match their goals with – Environment obstacles – other passengers goals – implicit behavioral rules that govern the social interaction in underground stations
  • 41. SCA model of Underground Station Scenario Spatial structure of the environment Spatial structure  discrete abstraction of simulation environment
  • 42. SCA model of Underground Station Scenario Active Elements of the Environment and Field Types
  • 43. SCA model of Underground Station Scenario Active Elements of the Environment and Field Types
  • 44. SCA model of Underground Station Scenario Active Elements of the Environment and Field Types
  • 45. SCA model of Underground Station Scenario Agent types An agent type t is a triple <∑t , Perceptiont, Actiont> where A Passengers Dynamic g Agents • ∑t : set of agent states e • Perceptiont : specifies for every agent n state and field type t Seat – a sensitivity coefficient c modulating (amplifies/attenuates) field values T Station Exit – a sensibility threshold t filtering out y Static Agents fields that are considered too faint p Wagon Exit (active – An agent perceives a field fT when e objects) CompareT(f*c,t) = true s • Actiont : behavioral specification for Handles agents of type t
  • 46. SCA model of Underground Station Scenario Passengers behavior as state-transition diagram and attitudes towards movement E Example based on designed behaviors for the case study. G Should be calibrated SCA platform editor of agents’ behaviors W S P Seated: agent seated on a seat W Waiting: passengers on the platform S of the wagon waiting for a train G Get Off: people on the wagon that have to State Transition get off the train Passenger: agent on the train that has no P immediate necessity to get off Exit: passenger that has got down the train E and goes away from the station
  • 47. SCA model of Underground Station Scenario Mobile Agents – Movement When multiple signals are perceived, agents evaluate next-destination site according to weighted sum of perceived values transport(p,q) S tate E xits D oors S eats H andles P resence E xit press. ∑ W - Attract (2) - - Repel (3) Repel (1) P - - Attract (1) Attract (2) Repel (3) Repel (2) G - Attract (1) - - Repel (2) - S - Attract (1) - - - - E Attract (1) - - - Repel (2) -
  • 48. SCA model of Underground Station Scenario (demo) Field “Handle” 1) Pedestrian agent in site P (on the wagon)
  • 49. SCA model of Underground Station Scenario (demo) E mit (s , f, p) Field “Handle” 1) Pedestrian agent in site P (on the wagon) 1) Available seats (only one) emit a field that is perceived as attractive by the agent Field “Seat”
  • 50. SCA model of Underground Station Scenario (demo) E mit (s , f, p) 1) Pedestrian agent in site P (on the wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense Tras port (p,q)
  • 51. SCA model of Underground Station Scenario (demo) E mit (s , f, p) 1) Pedestrian agent in site P (on the wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense Tras port (q,t) 1) Process iterated until the agent reaches the site where the local max of intensity is perceived
  • 52. SCA model of Underground Station Scenario (demo) E mit (s , f, p) 1) Pedestrian agent in site P (on the wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense Tras port (t,r) 1) Process iterated until the agent reaches the site where the local max of intensity is perceived
  • 53. SCA model of Underground Station Scenario (demo) E mit (s , f, p) 1) Pedestrian agent in site P (on the wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense Trasport (r,g) 1) Process iterated until the agent reaches the site where the local max of intensity is perceived
  • 54. SCA model of Underground Station Scenario (demo) E mit (s , f, p) 1) Pedestrian agent in site P (on the wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the Tras port (g,k) adjacent site, e.g. where the field is more intense 1) Process iterated until the agent reaches the site where the local max of intensity is perceived
  • 55. SCA model of Underground Station Scenario (demo) 1) Pedestrian agent in site P (on the R eact (a,s s,o) wagon) 2) Available seats (only one in site Q) emit a field that is perceived as attractive by the agent if distance(P,Q)<ped_threshold 3) The agent perceives the field and moves (by a transport action) to the adjacent site, e.g. where the field is more intense R eact (p,s g,s ) 4) Process iterated until the agent reaches the site where the local max of intensity is perceived 3) Available_seat agent and pedestrian agent change their state simultaneously (agent seat turns into the occupied state and stops emitting fields while passenger turns into state seated)
  • 56. Underground Case Study: model execution • Simulation configuration – 6 agents getting off – 8 agents getting on
  • 57. Visualization of system dynamics 3D rendering of 2D simulations (offline animation) • Java based bidimensional simulator • Exported log of the simulation including – Definition of the spatial structure – System dynamics • MaxScript that allows 3D Studio Max to generate an animation representing the simulated scenario Avatar#001#001#001#004#003#000@ Avatar#002#002#001#003#005#000@ Avatar#001#002#010#003#002#000@ Avatar#002#001#001#003#004#000@ Space#001#001#001#001#006#000@ Space#001#002#001#002#005#000@ Space#001#003#001#004#005#000@ Space#001#004#001#004#004#000@ Space#001#005#001#003#004#000@ Space#001#006#001#003#002#000@ Space#001#007#001#004#002#000@ Space#001#008#001#005#002#000@
  • 58. Why 3D? • To obtain an effective visualization of simulation dynamics • To obtain a machine-readable spatial abstraction in a semi- automatic way from existing models of the environment • To exploit the rich information of a 3D model to implement highly realistic perception
  • 59. 59
  • 60. Freezing by Heating - Experiments • 3 densities (20-40-60%) • 10 simulations for each densities • After few turns pedestrians-agents get stacked in front of the exit (80-90% of pedestrian population can’t move for the turn) Schadschneider Helbing
  • 61. Lane Formation: Experiments • Simple Behavioural Model: pedestrians are attracted by the desired exit (Without collision avoidance the phenomenon of freezing by heating is detected also at low densities) • Introduction of repulsion: – Each pedestrian at the beginning of the turn emits a presence field that is spread in adjacent sites – Each pedestrian evaluates negatively the sites where it is perceived the presence of other pedestrians (presence of pedestrians with opposite direction is evaluated more negatively) • Lane Formation only at low densities
  • 62. Lane Formation: Experiments • Introduction in the model of the 1,2 possibility of an exchange of position between pedestrians that 1 want to occupy one the site of the Togawa Simulation 1 Pedestrian Speed (Sites Per Turn) other 0,8 Simulation 2 Simulation 3 • Introduction of a concept of 0,6 Simulation 4 Blue&Adler “irritation” that leads pedestrians to: 0,4 – Search for new paths (empty sites become more desirable after some 0,2 turns of immobility) 0 – Attempt to exchange position with 10 20 30 40 50 60 70 80 Pedestrian Density (Pedestrians / Total Sites) 90 95 100 other agents (less sensitivity to presence field of other pedestrians) 4 different configurations of 3 different corridor • Experimentations performed parameters (2 experiments for geometries each density, 500 turns each) with density from 10 to 90%:
  • 63. Evacuation scenarios S ingola uscita D ue uscite
  • 64. …… continues …… I Thank You