Multi-grid simulation of pedestrian counter flow with topological interaction

We investigate the dynamics of pedestrian counter flow by using a multi-grid topological pedestrian counter flow model. In the model, each pedestrian occupies multi- rather than only one grid, and interacts with others in the form of topological interaction, which means that a moving pedestrian interacts with a fixed number of those nearest neighbours coming from the opposite direction to determine his/her own moving direction. Thus the discretization of space and time are much finer, the decision making process of the pedestrian is more reliable, which all together makes the moving behaviour and boundary conditions much more realistic. When compared with field observations, it can be found that the modified model is able to reproduce well fitted pedestrian collective behaviour such as dynamical variation of lane formation, clustering of pedestrians in the same direction, etc. The fundamental diagram produced by the model fits also well with field data in the free flow region. Further analyses indicate that with the increase of the size of pedestrian counter flow system, it becomes harder for the system to transit into a jamming state, while the increase of interaction range does not change the transition point from free flow to jamming flow in the multi-grid topological counter flow model. It is also found that the asymmetry of the injection rate of pedestrians on the boundaries has direct influence on the process of transition from free flow to jamming flow, i.e., a symmetric injection makes it easier for the system to transit into jamming flow.

[1]  Yuji Hasemi,et al.  EXPERIMENTAL VALIDATION OF MOTOR SCHEMA-BASED CELLULAR AUTOMATON MODEL FOR PEDESTRIAN DYNAMICS , 2007 .

[2]  D. Helbing.  Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[3]  R. Jiang,et al.  Pedestrian behaviors in a lattice gas model with large maximum velocity , 2007 .

[4]  A. Seyfried,et al.  The fundamental diagram of pedestrian movement revisited , 2005, physics/0506170.

[5]  G. Parisi,et al.  Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study , 2007, Proceedings of the National Academy of Sciences.

[6]  William H. K. Lam,et al.  Pedestrian speed/flow relationships for walking facilities in Hong Kong , 2000 .

[7]  Anders Johansson,et al.  Self-organization and optimization of pedestrian and vehicle traffic in urban environments , 2008 .

[8]  Wang Bing-Hong,et al.  Evacuation behaviors at exit in CA model with force essentials: A comparison with social force model , 2006 .

[9]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  T. Nagatani,et al.  Jamming transition in pedestrian counter flow , 1999 .

[11]  Dai Shi-qiang,et al.  Subconscious Effect on Pedestrian Counter Flow , 2008 .

[12]  Liu Mu-Ren,et al.  Lattice-gas simulation of escaping pedestrian flow in corridor , 2004 .

[13]  Huanhuan Tian,et al.  INFLUENCE OF INFORMATION ON CROWD DISPERSION PROCESS , 2009 .

[14]  Dirk Helbing,et al.  Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data , 2007, Adv. Complex Syst..

[15]  Dirk Helbing,et al.  Experimental study of the behavioural mechanisms underlying self-organization in human crowds , 2009, Proceedings of the Royal Society B: Biological Sciences.

[16]  Dirk Helbing,et al.  Dynamics of crowd disasters: an empirical study. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Guo Ren-Yong,et al.  Logit-based exit choice model of evacuation in rooms with internal obstacles and multiple exits , 2010 .

[18]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[19]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[20]  Tao Chen,et al.  Lattice gas simulation and experiment study of evacuation dynamics , 2008 .

[21]  William H. K. Lam,et al.  New Level-of-Service Standard for Signalized Crosswalks with Bi-Directional Pedestrian Flows , 2005 .

[22]  Akihiro Nakayama,et al.  Instability of pedestrian flow and phase structure in a two-dimensional optimal velocity model. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Y. F. Yu,et al.  Cellular automaton simulation of pedestrian counter flow considering the surrounding environment. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  S. Dai,et al.  Centrifugal force model for pedestrian dynamics. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  A. Seyfried,et al.  Basics of Modelling the Pedestrian Flow , 2005, physics/0506189.

[26]  Jun Zhang,et al.  k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow , 2010 .

[27]  Bing-Hong Wang,et al.  Simulation of evacuation processes using a multi-grid model for pedestrian dynamics , 2006 .

[28]  Takashi Nagatani,et al.  Pattern formation and jamming transition in pedestrian counter flow , 2002 .

[29]  Shao Chun-fu,et al.  Simulation of bi-directional pedestrian flow based on cellular automata model , 2008 .