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Adaptive pedestrian behaviour for the preservation of group cohesion: observations and simulations

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

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Adaptive pedestrian behaviour for the preservation of group cohesion: observations and simulations

  1. 1. Adaptive pedestrian behaviour for the preservation of group cohesion: observations and simulations Giuseppe Vizzari ! Complex Systems and Artificial Intelligence Research Center (CSAI) University of Milano-Bicocca, Italy !
  2. 2. Outline • Pedestrian and crowd simulation: why groups? • Some observations and empirical data • An adaptive pedestrian model for preserving group cohesion • The model in a benchmark scenarios • Conclusions and discussion TRB 2014 - Washington DC - Jan. 12, 2014
  3. 3. Impact of groups in pedestrian and crowd dynamics • Most modelling approaches generally consider every pedestrian as a individual with no relationships • Considering only his/her own goals • Considering other pedestrians as moving obstacles • Nonetheless, in several situations pedestrians are bound by relationships influencing their movement • Generally speaking, a crowd is made up of groups of pedestrians... • What do we miss by neglecting this aspect of pedestrian behaviour? TRB 2014 - Washington DC - Jan. 12, 2014
  4. 4. Groups in the literature - Observations • At least two studies report observations about groups • Willis A, Gjersoe N, Havard C, Kerridge J, Kukla R, 2004, "Human movement behaviour in urban spaces: implications for the design and modelling of effective pedestrian environments" Environment and Planning B: Planning and Design 31(6) 805 – 828 • Michael Schultz, Christian Schulz, and Hartmut Fricke. “Passenger Dynamics at Airport Terminal Environment”, Pedestrian and Evacuation Dynamics 2008, Springer-Verlag, 2010 • Observations carried out in low density conditions • Groups of small size were most frequently observed TRB 2014 - Washington DC - Jan. 12, 2014
  5. 5. Groups in the literature Modeling and Simulation • Extensions to the social force model • Helbing, Theraulaz et al. 2009, 2010 • Small groups (2,3,4), unstructured • Low to moderate densities • Validation based on actual observations • Xu and Duh, 2010 • Only couples (groups of 2 pedestrians) • Low to moderate densities • Shallow validation based on literature (Daamen, 2004) • CA models • Sarmady, Haron, Zawawi Hj, 2009 • Leaders and followers • Groups of 2 to 6 members experimented • Not validated • Agent-based models • Qiu and Hu 2010 • Structured groups (intra and inter group matrices) • Large groups experimented (60 pedestrians) • Not validated • Group members tend to stay close to other group members (additional behavioural component) TRB 2014 - Washington DC - Jan. 12, 2014
  6. 6. Admission test University of Milano-Bicocca • Admission test of the Faculty of Psychology at the University of MilanoBicocca - September 1, 2011 • Counting activity supported by video footages of the event • About two thousand students attended the test • About 34% individuals, 50% couples, 13% triples and 3% groups of 4 members (!) • Statistically validated relationship between group size and velocity • Additional quantitative analyses about the arrival and entrance process, LOS • Qualitative analysis of group shapes and related phenomena • More details in an ACRI 2012 (C&CA) and upcoming PED 2012 paper TRB 2014 - Washington DC - Jan. 12, 2014
  7. 7. Vittorio Emanuele II Gallery, Milan • Popular commercial-touristic walkway in Milan’s city centre • Goals of the survey: • level of density and walkway level of service (A and B); • presence of groups (over 84%); • group size and proxemics spatial patterns, trajectories and walking speed (groups are slower but their trajectories are shorter); • group proxemics dispersion (they preserve cohesion, even if large ones occupy more space) • still hard to evaluate spatial arrangement of group members TRB 2014 - Washington DC - Jan. 12, 2014 Group   dispersion Couples Triples 4 Members Distance   Centroid 0.58 m   (sd 0,22) 0.76 m   (sd 0,11) 0.67   (sd 0.12)
  8. 8. A model considering groups • Based on the floor-field CA approach, with significant difference on movement choice • Employing traditional factors for movement destination choice • Goal orientation • Presence of obstacles • Presence of other pedestrians (basic proxemics) • A notion of group has been introduced • To generate a generalised effect of cohesion among members of groups • ... able to overcome goal orientation for certain types of groups (e.g. families, close friends) • Speed heterogeneity also introduced (poster on Monday afternoon) TRB 2014 - Washington DC - Jan. 12, 2014 Considered factors: + cell is closer to pedestrian's goal (voided by high group dispersion) + presence of group members nearby - presence of other pedestrians nearby - presence of obstacles nearby Movement blocking factors: - cell is occupied by another pedestrian (but in very high densities the cell can be shared by two pedestrians) - cell is occupied by an obstacle
  9. 9. A few formal details • Stochastic choice of destination cell; for each cell c, the probability of choosing an action a leading to it is
 • The “utility” value of the cell is defines as follows:
 
 
 
 where Goal is associated to the static floor field and Obs to the wall potential Sep is associated to the proxemic repulsion D is an inertia factor Over regulates the possibility of having two pedestrians sharing the same cell in case of high density • Coh and Inter represent group cohesion factors respectively for small simple groups and large potentially structured groups • • • • TRB 2014 - Washington DC - Jan. 12, 2014
  10. 10. Overlapping • Overlapping is a transient situation in which pedestrians share the same cell • ... it can sometimes be observed in counterflow situations in which there is not enough space for avoidance • It can only happen if local density exceeds a given threshold • The choice is still penalised (Over ≤ 0) • No more than two pedestrians can share a single cell TRB 2014 - Washington DC - Jan. 12, 2014 [Kretz et al., 2006]
  11. 11. Simple and structured groups • Simple groups are made up of family members, friends, people that know each other • They often adapt their behaviour to preserve the cohesion of the group (b) B • Large groups can include perfect strangers that share for some time a common goal • Members of this group have a tendency to stay close to each other... • ... but this tendency is not so strong to prevent group fragmentation • And generally they are actually structured (they can include other often simple - groups), so we call them structured (c) U Figure 4. Snapshots from the expe TRB 2014 - Washington DC - Jan. 12, 2014
  12. 12. [12] Recent works represent a relevant effort towards the modeling of groups, respectively in particle-based [9, 18] and in agent-based approaches [11]: in all these approaches, groups are modeled by means of additional contributions to the overall pedestrian behaviour representing the tendency to stay close to other group members. However, most of the above approaches only deal with small groups in relatively low density conditions or they were not ofvalidated against real data. the different components of movement “utility” are Adaptive group cohesion mechanism • Multipliers according to the state of the group ! Balance(k) = ! ! 8 > 1 · k + ( 2 · k · DispBalance) >3 3 < 1 >3 > :k · k + ( 2 · k · (1 3 adjusted if k = kc DispBalance)) if k = kg _ k = ki otherwise 4.2 Finer Scale of Discretization [8] [13] • The dispersion of the group causes an increased impact of simple group 4.3 Different Types of Pedestrians cohesion and a reduced effect of goal attraction (static floor field) [8] [17] 4.4 Conclusions and Future Developments ACKNOWLEDGMENTS REFERENCES [1] Victor J. Blue and Jeffrey L. Adler. (2000). Modeling four-directional pedestrian flows. Transportation Research Record, 1710:20–27. 10 TRB 2014 - Washington DC - Jan. 12, 2014
  13. 13. Modelling groups - some qualitative results Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor TRB 2014 - Washington DC - Jan. 12, 2014
  14. 14. Aggregate effects of groups Counterflow of two structured groups including simple groups of various size, in a 2.4 m wide corridor;
 shuffled sequential update - ongoing tests with parallel update strategy TRB 2014 - Washington DC - Jan. 12, 2014
  15. 15. Aggregate effects of groups analysed • We can interpret the results making considering two phenomena 1.Wide groups offer a large profile to the counter flow, so they have a higher probability of facing conflicts 2.Once a group has formed a line, instead, the leader has the same conflict probability of an individual, but the follower has often an advantage • In low density situations phenomenon (1) prevails, leading to a lower average combined flow for groups of pedestrians whose size is larger than 2 • Pairs in fact can easily form a line, turning phenomenon (1) to (2) • In high density situations the probability of facing conflicts is very high also for individuals, so phenomenon (2) prevails, leading to higher average combined flow for even large groups (size 5) TRB 2014 - Washington DC - Jan. 12, 2014
  16. 16. Effectiveness of simple group cohesion mechanism Counterflow of two structured groups including simple groups of various size, in a 3.6 m wide corridor (Dispersion measured in terms of area covered by the group) TRB 2014 - Washington DC - Jan. 12, 2014
  17. 17. Additional results in “experimental” scenarios: T junction 4 J Zhang, W Klingsch, A Schadschneider, and A Seyfried 5 T-240-100-240-right T-240-100-240-left 4 3 y [m] 2 1 0 -1 -2 -3 -6 -4 -2 0 2 4 x [m] (a) Snapshot (b) Pedestrian trajectories Fig. 2. Trajectories and snapshot from T-junction experiment. From these trajectories, pedestrian characteristics including flow, density, velocity and individual distances at any time and position can be determined. 3 Experiment analysis Plot of experimentally observed data
 [Zhang et al., 2012] TRB 2014 - Washington DC - Jan. 12, 2014 In previous studies, different measurement methods were used to limit the comparability and fluctuation of the data. E.G. Helbing et al. proposed a Gaussian, distance-dependent weight function [13] to measure the local density and local velocity. Predtechenskii and Milinskii [14] used a dimensionless definition to consider different body sizes and Fruin introduced the ”Pedestrian Area Module” [15]. This study focuses on the Voronoi method proposed in [16, 17], where the density distribution can be assigned to each pedestrian. This method permits examination on scales smaller than the pedestrians for its high spatial resolution. [Vizzari et al., 2013] 3.1 Measurement methodology
  18. 18. Conclusions and discussion • Groups are relevant and significant • Results of simulations are partly validated • Fundamental diagram and spatial utilisation in tune with results from the literature… without groups • Group cohesion mechanism generates results about dispersion that are in tune with Vittorio Emanuele Gallery’s observation… • … but we don’t have data about groups in high density situations (and it’s hard to obtain such data) • More observations, experiments and simulations are necessary to improve our understanding of the phenomenon • Closer collaboration between researchers working on synthesis and analysis of crowds is promising and possibly beneficial for both TRB 2014 - Washington DC - Jan. 12, 2014
  19. 19. ありがとうございます。 Giuseppe Vizzari

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