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

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.

[1]  E. Hall,et al.  The Hidden Dimension , 1970 .

[2]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing, 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I , 2010, International Conference on Artificial Intelligence and Soft Computing.

[3]  M. Baucus Transportation Research Board , 1982 .

[4]  G. L. Bon,et al.  Scientific Literature: The Crowd. A Study of the Popular Mind , 1897 .

[5]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

[6]  Stefania Bandini,et al.  An Agent Model of Pedestrian and Group Dynamics: Experiments on Group Cohesion , 2011, AI*IA.

[7]  Roberto Pirrone,et al.  AI*IA 2011: Artificial Intelligence Around Man and Beyond - XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011. Proceedings , 2011, AI*IA.

[8]  Stefania Bandini,et al.  Situated Cellular Agents: A Model to Simulate Crowding Dynamics , 2004, IEICE Trans. Inf. Syst..

[9]  Mara Chagas Diogenes,et al.  Pedestrian Counting Methods at Intersections , 2007 .

[10]  Partha Chakroborty,et al.  Comparison of Pedestrian Fundamental Diagram across Cultures , 2009, Adv. Complex Syst..

[11]  Erica D. Kuligowski,et al.  Pedestrian and Evacuation Dynamics , 2011 .

[12]  Sabiha Amin Wadoo,et al.  Pedestrian Dynamics: Feedback Control of Crowd Evacuation , 2008 .

[13]  S. Reicher,et al.  The Psychology of Crowd Dynamics , 2008 .

[14]  Jaroslaw Was,et al.  Crowd Dynamics Modeling in the Light of Proxemic Theories , 2010, ICAISC.

[15]  Andreas Schadschneider,et al.  CA Approach to Collective Phenomena in Pedestrian Dynamics , 2002, ACRI.

[16]  Marco Costa,et al.  Interpersonal Distances in Group Walking , 2010 .

[17]  M. Baldassare Human Spatial Behavior , 1978 .