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Modeling Pedestrian and Crowd Behaviour: the case of the Crystals Project

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Modeling Pedestrian and Crowd Behaviour: the case of the Crystals Project

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Modeling Pedestrian and Crowd Behaviour: the case of the Crystals Project

  1. 1. Modeling Pedestrian and Crowd Behaviour: the case of the Crystals Project Giuseppe Vizzari1,2 1Complex Systems and Artificial Intelligence Research Center University of Milano-Bicocca 2Crystals Project, Center of Research Excellence in Hajj and Omrah (Hajjcore) Umm Al-Qura University, Makkah, Saudi Arabia
  2. 2. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  3. 3. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  4. 4. The Hajj in Brief • Annual pilgrimage to Makkah, Saudi Arabia • Fifth pillar of Islam, a religious duty that must be carried out at least once in their lifetime by every able-bodied Muslim who can afford to do so • Over 2,5 millions of people coming from over 150 countries • A precise and articulated system of rituals implying the mass movement of pilgrims over several sites that in some cases are about 20 km distant
  5. 5. The Mashaer Line • Five proposed rail lines connecting the holy sites with one another and with Makkah • The southern rail includes 9 stations: 3 in Mina, 3 in Muzdalifah and 3 in Arafat, to replace 35,000 cars and buses and access the Haram and Makkah Central Area • Future lines to the Holy Haram • Extend the southern rail line to Jeddah Airport, with an elevated alignment above the Jeddah Expressway over an 80 Km length
  6. 6. Observations at the Hajj
  7. 7. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  8. 8. Observations at the Hajj - Groups as a crowd management organizational instrument • Pilgrims are subdivided into groups of 250 persons following a leader in their movement from the nearby tents area to the platform • The waiting boxes act as waiting areas hosting groups waiting to use ramps or elevators • The platform can safely host even more than 3000 pilgrims (the capacity of a train), but the process is aimed at avoiding overcrowding of the platform
  9. 9. Observations at the Hajj - Considerations • Groups are used as an organizational instrument to manage crowd • Group arrival is planned, scheduled • Leaders decide when and where to move, collaborating with station officers • Their size is relatively large, their cohesion is not extreme... • ... but inside them smaller sub- groups can be identified and they can be much more compact • Groups have different intermediate movement targets, although the same final goal
  10. 10. Group influence in general - Considerations • The presence of groups is pervasive in many events involving large crowds • Groups are simply out there... • ... it’s not a matter of deciding if they’re ‘good’ or ‘bad’ for the pedestrian flow • ... it’s a matter of understanding their impact, in different relevant conditions • The presence of groups should be carefully considered: • Design choices might make it difficult for a group to preserve its cohesion, which is particularly significant in certain situations (e.g. kids, elderly, mobility impaired persons)... • ... and this would cause stress in group members and congestions, delays in the whole system
  11. 11. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  12. 12. Groups in the literature - Observations • At least two studies report observations about groups • Willis A, Gjersoe N, Havard C, Kerridge J, Kukla R, 2004, "Human movement behaviour in urban spaces: implications for the design and modelling of effective pedestrian environments" Environment and Planning B: Planning and Design 31(6) 805 – 828 • Michael Schultz, Christian Schulz, and Hartmut Fricke. “Passenger Dynamics at Airport Terminal Environment”, Pedestrian and Evacuation Dynamics 2008, Springer-Verlag, 2010 • Observations carried out in low density conditions • Groups of small size were most frequently observed
  13. 13. 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)
  14. 14. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  15. 15. The Crystals Project Approach “In silico” “In vitro” “In vivo”
  16. 16. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  17. 17. Experiments in Tokyo • Experiments carried out by the Research Center on Advanced Science and Technology of The University of Tokyo • Aimed at evaluating the impact of the presence of groups in experimental situations • Specifically their impact on the formation of lanes and total travel times in relatively high density situations • Results still not published... • ... However, we can already say that more experiments and observations are needed to draw conclusions • The influence of groups is not trivial
  18. 18. Admission test University of Milano-Bicocca • Admission test of the Faculty of Psychology at the University of Milano- Bicocca - September 1, 2011 • Counting activity supported by video footages of the event • About two thousand students attended the test • About 34% individuals, 50% couples, 13% triples and 3% groups of 4 members (!) • Statistically validated relationship between group size and velocity • Additional quantitative analyses about the arrival and entrance process, LOS • Qualitative analysis of group shapes and related phenomena
  19. 19. Vittorio Emanuele II Gallery, Milan • Popular commercial-touristic walkway in Milan’s city centre • Goals of the survey: • level of density and walkway level of service (A and B); • presence of groups (over 84%); • group size and proxemics spatial patterns, trajectories and walking speed (groups are slower but their trajectories are shorter); • group proxemics dispersion (they preserve cohesion, even if large ones occupy more space) • still hard to evaluate spatial arrangement of group members Group dispersion Couples Triples 4 Members Distance Centroid 0.58 m (sd 0,22) 0.76 m (sd 0,11) 0.67 (sd 0.12)
  20. 20. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  21. 21. A model considering groups • Based on the floor-field CA approach, with significant difference on movement choice • Employing traditional factors for movement destination choice • Goal orientation • Presence of obstacles • Presence of other pedestrians (basic proxemics) • A notion of group has been introduced • To generate a generalised effect of cohesion among members of groups • ... able to overcome goal orientation for certain types of groups (e.g. families, close friends) • Speed heterogeneity also introduced (poster on Monday afternoon)
  22. 22. A few formal details • Stochastic choice of destination cell; for each cell c, the probability of choosing an action a leading to it is • The “utility” value of the cell is defines as follows: where • Goal is associated to the static floor field and Obs to the wall potential • Sep is associated to the proxemic repulsion • D is an inertia factor • Over regulates the possibility of having two pedestrians sharing the same cell in case of high density • Coh and Inter represent group cohesion factors respectively for small simple groups and large potentially structured groups
  23. 23. Overlapping • Overlapping is a transient situation in which pedestrians share the same cell • ... it can sometimes be observed in counterflow situations in which there is not enough space for avoidance • It can only happen if local density exceeds a given threshold • The choice is still penalised (Over ≤ 0) • No more than two pedestrians can share a single cell [Kretz et al., 2006]
  24. 24. Simple and structured groups • Simple groups are made up of family members, friends, people that know each other • They often adapt their behaviour to preserve the cohesion of the group • Large groups can include perfect strangers that share for some time a common goal • Members of this group have a tendency to stay close to each other... • ... but this tendency is not so strong to prevent group fragmentation • And generally they are actually structured (they can include other - often simple - groups), so we call them structured
  25. 25. • Multipliers of the different components of movement “utility” are adjusted according to the state of the group • The dispersion of the group causes an increased impact of simple group cohesion and a reduced effect of goal attraction (static floor field) Adaptive group cohesion mechanism
  26. 26. Modelling groups - some qualitative results Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor
  27. 27. Aggregate effects of groups Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor; shuffled sequential update - ongoing tests with parallel update strategy
  28. 28. Aggregate effects of groups analysed • We can interpret the results making considering two phenomena 1.Wide groups offer a large profile to the counter flow, so they have a higher probability of facing conflicts 2.Once a group has formed a line, instead, the leader has the same conflict probability of an individual, but the follower has often an advantage • In low density situations phenomenon (1) prevails, leading to a lower average combined flow for groups of pedestrians whose size is larger than 2 • Pairs in fact can easily form a line, turning phenomenon (1) to (2) • In high density situations the probability of facing conflicts is very high also for individuals, so phenomenon (2) prevails, leading to higher average combined flow for even large groups (size 5)
  29. 29. Effectiveness of simple group cohesion mechanism Counterflow of two structured groups including simple groups of various size, in a 3.6 m wide corridor (Dispersion measured in terms of area covered by the group)
  30. 30. Additional results in “experimental” scenarios: T junction Plot of experimentally observed data [Zhang et al., 2012] [Vizzari et al., 2013]
  31. 31. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  32. 32. A sample simulation in the Arafat I station
  33. 33. Hajj case study: two vs. three groups
  34. 34. Hajj case study: normal vs. obstacle
  35. 35. Hybrid Agent Architecture OPERATIONAL LEVEL WALKING INTERACTION WITH OTHER TRAFFIC PERFORMING ACTIVITY BODY STRATEGICAL LEVEL TACTICAL LEVEL CHOICE OF ACTIVITIES SCHEDULE OF ACTIVITIES CHOICE OF ACTIVITY AREA ROUTE CHOICE MIND REACTIVECOGNITIVE
  36. 36. Additional Annotation Tools
  37. 37. Cognitive Map
  38. 38. Knowledge: Body + Mind
  39. 39. AGENT LIFE-CYCLE BODY MIND Perception Perception & Choose movement Self Localization Wayfinding Change Pathfield intermediate destination? Next Plan's Instruction yes yes no Plan Translation no Move (if possible) next turn got lost or don't know how to reach the goal? BODY Perception Choose movement Move (if possible) next turn • Plan Building • Plan Management • Actuation
  40. 40. Sample simulation considering crowd management procedures
  41. 41. Dynamically managing The tactical level: Introduction to the paths tree • Given an arbitrary environment, the agent should be able to plan a path toward its target, considering: • The types of environment that will be crossed  static elements • The emergence of congestion or other elements influencing the path conditions  dynamic elements • The choice among paths is performed according to the expected traveling time, dynamically changing. • The decision tree contains the average traveling time of each minimal path to a destination, estimated by considering static elements and the average speed of the agents.
  42. 42. An evacuation of a large population of pedestrians 1000agentsat~10p/sec 1000agentsat~10p/sec Step 50 Step 200 Step 350 Step 500
  43. 43. Quantitative results
  44. 44. Outline • The context of application: the Hajj, the Mashaer line and the Arafat I station • Groups, as a crowd management concept and a natural, pervasive presence in pedestrian population • Groups in the relevant literature • “In vitro”, “in vivo”, “in silico”: the Crystals Project approach • Observations about groups (“in vitro” and “in vivo”) • Modeling and simulation (“in silico”) • Results in the Arafat I scenario • Conclusions and future developments
  45. 45. Conclusions and discussion • Groups are relevant and significant • Results of simulations are partly validated • Fundamental diagram and spatial utilisation in tune with results from the literature… without groups • Group cohesion mechanism generates results about dispersion that are in tune with Vittorio Emanuele Gallery’s observation… • … but we don’t have data about groups in high density situations (and it’s hard to obtain such data) • More observations, experiments and simulations are necessary to improve our understanding of the phenomenon • Closer collaboration between researchers working on synthesis and analysis of crowds is promising and possibly beneficial for both
  46. 46. Future works • Of course, improvements... • of the model, of our understanding about the phenomena... • Of course, additional applications to real world scenarios... • for providing additional success stories and collecting additional issues, limits, directions for improvements • Strengthening the connections with automated analysis/computer vision • Exploiting the model for supporting smart environment, smart city systems • In the vein of what was discussed by Ulrich Wagoum for stadium evacuation assistant, Georgios Sirakoulis work on anticipative technologies and robotic evacuation assistant...
  47. 47. ありがとうございます。 Giuseppe Vizzari

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