Data Collection for Modeling and Simulation: Case Study at the University of Milan-Bicocca

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1 Data Collection for Modeling and Simulation: Case Study at the University of Milan-Bicocca Mizar Luca Federici 1, Andrea Gorrini 2, Lorenza Manenti 3, and Giuseppe Vizzari 3 1 CROWDYXITY s.r.l. - Crowd Dynamics and Complexity Via Ventura 3, Milano (Italy) m.federici@crowdyxity.com 2 INFORMATION SOCIETY Ph.D. Program University of Milan-Bicocca Via Bicocca degli Arcimboldi 8, Milano (Italy) a.gorrini@campus.unimib.it 3 CSAI - Complex Systems and Artificial Intelligence Research Center University of Milan - Bicocca Viale Sarca, 336, Milano (Italy) {manenti,vizzari}@disco.unimib.it Abstract. The investigation of crowd dynamics is a complex field of study that involves different types of knowledge and skills, and, also from the socio-psychological perspective, the definition of crowd is still controversial. We propose to investigate analytically this phenomenon focusing on pedestrian dynamics in medium-high density situations, and, in particular, on proxemic behavior of walking groups. In this work we will present several results collected during the observation of the incoming pedestrian flows to an admission test at the University of Milano-Bicocca. In particular, we collected empirical data about: levels of density and of service, group spatial arrangement (degree of alignment and cohesion), group size and composition (gender), walking speed and lane formation. The statistical analysis of video footages of the event showed that a large majority of the incoming flow was composed of groups and that groups size significantly affects walking speed. Collected data will be used for an investigative modeling work aimed at simulating the observed crowd and pedestrian dynamics. Key words: Crowd, Pedestrian Dynamics, Groups, Proxemics 1 Introduction The investigation of crowd dynamics is a complex field of study, and it involves different types of knowledge and skills. Its scientific relevance is related to the growing need of applicative results, strictly linked to the management of large Authors are listed in alphabetical order

2 2 Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G. events (e.g., celebrations, concerts, sports events) and to the design of public spaces (e.g., squares, stadiums, stations). From the socio-psychological perspective the definition of crowd is still controversial. Moreover, it is difficult to establish standard guidances for the empirical investigation of the phenomenon due to its variability among size and typology. Early interest in studying crowd behavior started from the pioneering study of Gustave Le Bon [1] who defined a crowd as a potential threat to society: as members of a crowd people display an altered state of consciousness, with a consequent loss of sense of self-awareness and an increase of irrational and violent behaviors. Far from this approach the ESIM-Elaborated Social Identity Model [2] proposes a social-normative conception of collective behavior, claiming that social norms continue to shape behavior of people also within the crowd, by means of the spontaneous transition from an individual identity to a common social identity [3]. Starting from these divergent theoretical assumptions, the most accepted definition of crowd cites: A crowd can be defined as a gathering of 20 people (at least), standing in close proximity at a specific location to observe a specific event, who feel united by a common social identity, and who are able to act in a socially coherent way, despite being strangers in an ambiguous or unfamiliar situation [4, p.43]. Because of the difficulty to reach an exhaustive definition of what a crowd is, we propose to analytically investigate the phenomenon focusing on pedestrian dynamics in medium-high density situation. In particular, we focus on: groups 1, which are the basic constitutive elements of the crowd, and proxemics [5], chosen as an analytical indicator of crowd behavioral dynamics, thanks to its ability to model social relationships among people and groups in the crowd, by means of the dynamic regulation of interpersonal distances 2. Proxemic behavior reveals the psychological bonding among group members, and, in high-density situation, it represents an adaptive stress-reducing behavior to crowding 3, by producing spatial boundaries that shield group members from the invasion of personal space. In motion situation, group proxemic behavior generates typical patterns (see Fig. 1), which allow communication and spatial cohesion among members [9]. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction (line-abreast pattern); as the density increases, the linear walking formation turns into a V-like pattern, with the mid- 1 A group can be defined as two or more people who interact for a shared goal, perceiving a membership based on a shared social identity [3]. 2 The regulation of spatial distances (intimate, personal, social, and public distances) is influenced by age, gender, culture, and personality [6]. Previous studies in the pedestrian dynamics literature confirm that culture is an important factor that influence the dynamics of crowds and pedestrian flows [7]. 3 Psycho-physiological responses of arousal and stress, cognitive performance decrements, and aggressive response [8].

3 Data Collection for Modeling and Simulation 3 Fig. 1: The typical patterns of walking groups (from the left: line-abreast, V-like, river-like pattern) dle individual positioned slightly behind in comparison to the lateral individuals; in situation of high density, the spatial distribution of group members leads to river-like pattern and lane formation, characterized by the presence of a leader that coordinates the group members to cross the space [10]. To further investigate pedestrian dynamics in medium-high density situations, we propose to detect the behavior of walking groups, focusing on the relation among: level of density, proxemic spatial arrangement (degree of alignment and cohesion), walking speed, group size and composition (gender). In particular, in this work we will present several results collected during the observation of the incoming pedestrian flows to the admission test of the Faculty of Psychology at the University of Milano- Bicocca, which was performed in September 1st, In the context of crowd studies carried out by the CSAI research center, the survey was aimed at enhancing existing knowledge and results achieved from several early observations [11, 12], and at formalizing a first modeling proposal [10, 13, 14]. This work is organized as follow: after a brief description of the scenario and of the methodological approach (Sec. 2), a preliminary analysis will be proposed (Sec. 3); in particular we will focus on flow composition (Sec. 3.1) and on walking speed analysis (Sec. 3.2); then previous results on modeling and simulation of the entry process will be presented (Sec. 4). The paper ends with some final remarks and some suggestions for future developments (Sec. 5). 2 Scenario Analysis and Methodological Approach About two thousand of people attended the admission test to the Faculty of Psychology at the University of Milano-Bicocca 4. The survey was aimed at observing individual and group behavior during the inflow and entry processes into the Building U6, one of the venues of the admission test (see Fig. 2a). In 4 The subscription list of the participants to the admission test was composed of 2094 students, including 437 males (29%), and 1657 females (79%), on average 19 years old.

The size and the composition of unidirectional incoming pedestrian flow were measured every minute.

4 4 Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G. particular, data collection was focused on levels of density, group composition (size and gender), group spatial arrangement and group walking speeds. The size and the composition of unidirectional incoming pedestrian flow were measured every minute. The identification of groups in the streaming of participants was assessed on the basis of: visual contact, body orientation, gesticulation and talk and, in particular, on spatial arrangement and cohesion of members (in reference to typical proxemic patterns). A topographical analysis of the environment was performed to support the detection of the level of density, of the levels of service and of the walking speed analysis. The team was composed of two supervisors and six observers (distributed on different counting locations). Each counter was equipped with: a pre-printed blank table that was necessary to note data, a people counter and a chronograph. Several preliminary inspections were performed to define the best counting locations, trying not to hinder the activities of the organizers, and not to influence the behavior of observed subjects. To ensure more validity to the research video-recording tools were employed in addiction to headcounting. The equipment for video footage consisted of full HD video cameras with stands. Existing legislation about privacy was also consulted to exceed some ethical issues about the video recording of the event. (a) The buildings U6 and U7; the counting points (circles); unidirectional incoming flow (dotted line) (b) The area of the bidirectional flow between the building U6 and U7; the entry area of the building U6 (dotted line) Fig. 2: Scenario analysis: the incoming pedestrian flow and the entrance of building U6

5 Data Collection for Modeling and Simulation 5 3 Data Analysis The starting point of data analysis consisted of a preliminary investigation about these data (e.g., charts and tables of frequency). Then, a blind codification of the video-recorded images was performed by two independent coders to achieve average results. A preliminary comparison of the results obtained by the two techniques (head-counting and video analysis activities) showed that the video analysis, although time spending, is a useful technique to monitor human error, ensuring more validity to the research. Even if expert counters were employed during the activities, from the video footages analysis emerged the presence of an error of over estimation of the size and group composition of the incoming flow by counters, quantifiable around 4% in relation to its size and more than 10% in relation to groups detection. For this reason we decided to rely on the video footages 5 of the event. In the following, we will introduce several analysis performed taking into account video footages of the event: flow size and composition, and walking speed analysis. 3.1 Flow Size and Composition The incoming pedestrian flow was composed of 1897 persons, which reached the venues of the admission test between 7:35 a.m. and 8:40 a.m.. The 34% of them arrived alone, while the 66% arrived in groups: 77% of groups were couples, 19% triples and 4% larger groups. In relation to the average low-level of density observed (0,05 pers/m 2 ), the analysis of the typical spatial patterns of walking groups shows that (see Tab. 1): 97% of couples is characterized by line-abreast pattern, 3% by river-like pattern; 66% of triples is characterized by line-abreast pattern, 33% by V-like pattern, and 1% by river-like pattern; groups with four members were usually split into subunits of dyads, triads, and single individuals: 30% of four-person groups is characterized by rhombus-like pattern (one person heading the group, followed by a dyad and a single person), 21% of the groups split into two dyads, 21% line-abrest pattern; 14% triads followed by a single person, 7% single individual followed by a triad, 7% by V-like pattern. 3.2 Walking Speed Analysis In this section an analysis of a selected portion of the incoming flow is proposed, considering people that reached building U6 between 7:52 a.m. and 8:15 5 The use of people tracking tools although it constitutes a useful contribution in detecting spatial movements of pedestrian within the crowd, is not enough calibrated to recognize proxemic indicators among group members.

6 6 Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G. Couples Triples Four Person Groups Line abreast pattern 97% 66% 21% River-like pattern 3% 1% V-like pattern 33% 7% Rhombus-like pattern 30% Two-dyad pattern 21% Triad + single pattern 14% Single + triad pattern 7% Table 1: Percentages related to the typical spatial patterns of walking groups a.m. (39% of the total incoming flow persons). We propose below a data analysis of the walking speed of single pedestrians and groups, and, in particular, we focused on: level of service (referred to a precise area of 146,4 m 2, see Fig. 2b), group size, group spatial arrangement and gender of individuals and group members. According to the existing HCM Walkway Level of Service Criteria [15], the flow rate was measured as the relationship among pedestrian/minute/meter: the average flow rate (5,09 ped/min/m) belongs to A-level, while several time intervals belong to B-level. It has to be underlined that the available space was huge in relation to the flow and, therefore, the level of service of the free flows were as low as expected. LOS-B were further analyzed in relation to flow composition: considering time intervals belong to B-level, we extracted a subset of 201 pedestrians. Because of large groups were not regularly detected, we focused the analysis on singles (50 single pedestrians - 25% of the subset), couples (50 couples - 50%) and triples (17 triples - 25%). A first statistic analysis was devoted to the identification of the average walking speed for singles (M = 1,38 m/s), couples (M = 1,30 m/s) and triples (M = 1,21 m/s). Moreover, a one-way analysis of variance (ANOVA see Tab. 3) showed that the size of groups affects walking speed in situation of medium density (p<0,05). More in detail, the differences in walking speed between singles and couples, singles and triples, couple and triples, were confirmed by a T-test analysis (p<0,01). In conclusion, the results showed that, at a low level of social density, the more the size of the pedestrian groups, the lower the walking speed was. Several further T-test analyses showed the following results: spatial patterns: no significant differences in walking speed (p>0,05) between line abreast and river-like couples, and among line-abreast, V-like and river-like triples; gender: no significant differences in walking speed (p>0,05) between females and males walking alone (i), same and mixed gender s couples (ii), same and mixed gender triples (iii).

7 Data Collection for Modeling and Simulation 7 Fig. 3: ANOVA analysis of the difference of walking speeds among singles, couples and triples (the analysis was performed by means of IBM SPSS Statistics v.18) 4 Towards Entry Process Simulation The last part of observation was related to the pedestrian dynamics of the entry process to the Building U6, in order to collect empirical data in different levels of density. In fact, the entry process consisted of two phases, the first being the incoming flow at the entrances of the Building U6 and gathering before the opening of the entrances of the venue; and the second being the access of the participants to the building according to a scheduling managed by the University Authorities. Participants were sub-divided into groups accordingly to the classrooms where they had to perform the admission test. Groups of about twenty people at time where called and allowed to access the venue, this on the basis of the availability of the assigned classrooms. The entry process, therefore, was not homogeneous as entrances in use and waiting times could vary. During the entry process the density in proximity of the doors varied from high levels (4,4 p/m 2 ) to low levels (<1 p/m 2 ) in relation to the phases of the process. Considering the existing HCM queueing LOS [15], the level of service in proximity of the entrance varied from E-Level to A-level. Moreover, local vari-

8 8 Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G. ations of the level of density were detected in the environment, because of the dynamics triggered by called participants that needed to reach the doors crossing the standing crowd. This latter phenomenon is related to the emergence of lane formation, i.e., when groups of people move in opposite directions in a crowded environment, they spontaneously organize themselves into different lanes for each direction of travel [16]. The modeling of this specific dynamic is an on-going work. An agent based model has been implemented to simulate the scenario of the entry process. Simulation has been informed by observed data and it is now undergoing a validation phase. In Fig. 4a and Fig. 4b the real and simulated phenomenon of lane formation are shown. The platform used for the simulation is based on an agent based model in which the environment is discretized and managed according to Cellular Automata approach (see [17] for a complete explanation of the model): the floor field method is used to guide agents towards their goal, according to the behavioral rule related to avoid contact with obstacles and other agents. Fig. 4c, Fig. 4d, and Fig. 4e show respectively: the grid obtained by the discretization of the environment (the pentagonal blue shape represents the destinations of agents (external and inside the building), the green areas represent generation points of agents and red areas represents obstacles in the environment), the path grid obtained with floor field method and related to the blue rectangular shape that represent the area inside building U6, and the composition of the path field grids related to both destinations (external and internal area). 5 Discussion and Future Works The analysis of the video footages of the event showed that: the incoming flow to the admission test was composed of almost two thousands of people and that a large majority of it was composed of groups (couples, triples, and groups of four members). The results achieved can be compared to others similar observations [9], taking into account the different context where the observations took place. These preliminary results point out that it can be useful to further investigate the pedestrian dynamics starting from groups, which constitute the basic elements that compose the crowd. Results show that at a low level of density the larger is the size of the pedestrian groups, the lower is the walking speed. At a high level of density, it was possible to detect lane formation, an emergent phenomenon related to pedestrian dynamics in crowded environments. Moreover, it would be interesting to further investigate if overestimation error of counters was occasional or if it can be considered as systematic, considering different results achieved by observations performed in different contexts [18]. In the latter hypothesis, the specific percentage of error could be estimated by means of further investigations. This would help to apply correction to future data collection campaigns based on manual head counts. Future works would be aimed to collect empirical data about the relationship between walking speed and proxemic spatial arrangement of walking

Data Collection for Modeling and Simulation (a) In vivo-observation (c) Environment grid 9 (b) 3D representation (d) Path grid (e) Grids composition Fig.

groups in high-density situation (taking into account also age and gender differences, and larger groups).

which are strictly linked with emergent complex dynamics that are difficult to be detected. For these reason those scenario can be supported by using simulation techniques.

people who attend every year the admission test.

9 Data Collection for Modeling and Simulation (a) In vivo-observation (c) Environment grid 9 (b) 3D representation (d) Path grid (e) Grids composition Fig. 4: Observation, modeling and simulation of the entry process to the Building U6. groups in high-density situation (taking into account also age and gender differences, and larger groups). In high-density scenarios, the degree of freedom of spatial distribution of walking groups would be affected by the lack of space and the need to avoid physical contact with other individuals or groups. The entry process represents a typical scenario of high-density situation in which different and competitive behaviors between individuals and impromptu collectives (lanes) which are strictly linked with emergent complex dynamics that are difficult to be detected. For these reason those scenario can be supported by using simulation techniques. Moreover, with the grant of the University Authority, the final aim of the survey will be the design of applicative strategies related to a more efficient management of people who attend every year the admission test. The use of simulation tools for pedestrian dynamics would be an useful approach to deeply investigate the phenomenon, and to study innovative strategies to support the management of entrance of people during this kind of test. To exceed this objective, we will collaborate with the University Authority to the development of planning standards related to pedestrian circulation dynamics, and physical layout of the environment in congested situations (such as the reduction of the entry queues to the venues, and the reduction of the delay in transfer times or ingress/egress, by means of the use of barriers or signposting).

10 10 Federici, M.L., Gorrini, A., Manenti, L., Vizzari, G. Acknowledgement This work is partially been funded by the University of Milano-Bicocca, within the project Fondo d Ateneo per la Ricerca - anno 2010/2011. References 1. Le Bon, G.: The crowd: A study of the popular mind. Macmillian (1897) 2. Reicher, S.: The psychology of crowd dynamics. Blackwell handbook of social psychology: Group processes (2001) Turner, J.: Towards a cognitive redefinition of the social group. Cahiers de Psychologie Cognitive/Current Psychology of Cognition (1981) 4. Challenger, R., C.C., M., R.: Understanding Crowd Behaviours. Volume 1. Cabinet Office (2009) 5. Hall, E.: The Hidden Dimension. Bodley Head (1969) 6. Aiello, J.: Human spatial behavior. Handbook of environmental psychology 1 (1987) Chattaraj, U., Seyfried, A., Chakroborty, P.: Comparison of pedestrian fundamental diagram across cultures. Arxiv preprint arxiv: (2009) 8. Baum, A., Paulus, P.: Crowding. Handbook of environmental psychology 1 (1987) Costa, M.: Interpersonal distances in group walking. Journal of Nonverbal Behavior 34(1) (2010) Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE 5 ( ) 11. Peacock, R., Kuligowski, E., Averill, J.: Pedestrian and evacuation dynamics. Springer Verlag (2011) 12. Kachroo, P., Al-Nasur, S., Wadoo, S., Shende, A.: Pedestrian dynamics: Feedback control of crowd evacuation. Springer Verlag (2008) 13. Bandini, S., Manzoni, S., Vizzari, G.: Situated cellular agents: A model to simulate crowding dynamics. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E SERIES D 87(3) (2004) Was, J.: Crowd dynamics modeling in the light of proxemic theories. In Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M., eds.: ICAISC (2). Volume 6114 of Lecture Notes in Computer Science., Springer (2010) II Milazzo, N Rouphail, J.H.D.A.: Transportation research board. National Research Council, Washington, DC 113 (2000) 16. Katsuhiro Nishinari Andreas Schadschneider, A.K.: CA approach to collective phenomena in pedestrian dynamics. In: International Conference on Cellular Automata for Research and Industry. (2002) Bandini, S., Rubagotti, F., Vizzari, G., Shimura, K.: An agent model of pedestrian and group dynamics: Experiments on group cohesion. In Pirrone, R., Sorbello, F., eds.: AI*IA. Volume 6934 of Lecture Notes in Computer Science., Springer (2011) Diogenes, M., Greene-Roesel, R., Arnold, L., Ragland, D.: Pedestrian counting methods at intersections: a comparative study. Transportation Research Record: Journal of the Transportation Research Board 2002(-1) (2007) 26 30

arxiv: v1 [cs.ma] 22 Nov 2017

arxiv: v1 [cs.ma] 22 Nov 2017 Micro and Macro Pedestrian Dynamics in Counterflow: the Impact of Social Groups Luca Crociani, Andrea Gorrini, Claudio Feliciani, Giuseppe Vizzari, Katsuhiro Nishinari, Stefania Bandini arxiv:1711.08225v1