Empirical study of a unidirectional dense crowd during a real mass event

Many tragic crowd disasters have happened across the world in recent years, such as the Phnom Penh stampede in Cambodia, crowd disaster in Mina/Makkah, and the Love Parade disaster in Germany, showing that management of mass events is a tough task for organizers. The study of unidirectional flow, one of the most common forms of motion in mass activities, is essential for safe organization of such events. In this paper, the properties of unidirectional flow in a crowded street during a real mass event in China are quantitatively investigated with sophisticated active infrared counters and an image processing method. A complete dataset of flow rates during the whole celebration is recorded, and a time series analysis gives new insight into such activities. The spatial analysis shows that the velocity and density of the crowd are inhomogeneous due to the boundary effect, whereas the flux is uniform. The estimated capacity of the street indicates that the maximum flow rate under normal condition should be between 1.73 and 1.98 /m/s, which is in good agreement with several field studies available in the existing literature. In consideration of the significant deviation among different studies, fundamental diagrams of dense crowds are also re-verified, and the results here are consistent with those from other field studies of unidirectional flow, but different from the bidirectional and experimental results. It is suggested that the data from multidirectional flow and experiments cannot be directly applied to unidirectional dense flow in a real mass event. The results also imply that the density of a similar unidirectional marching crowd should be controlled to be under 5 /m2, which can produce optimal efficiency and have more possibility to ensure safety. The field study data given here provide a good example of a database for crowd studies.

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

[2]  A. Seyfried,et al.  Methods for measuring pedestrian density, flow, speed and direction with minimal scatter , 2009, 0911.2165.

[3]  Virginia Murray,et al.  Disasters at Mass Gatherings: Lessons from History , 2012, PLoS currents.

[4]  R. J. Wheeler,et al.  PEDESTRIAN FLOW CHARACTERISTICS , 1969 .

[5]  C. Willert,et al.  Digital particle image velocimetry , 1991 .

[6]  Rui Jiang,et al.  Unidirectional Pedestrian Flow in a Lattice Gas Model Coupled with Game Theory , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[7]  Bernhard Steffen,et al.  T-junction: Experiments, trajectory collection, and analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[8]  Eric Wai Ming Lee,et al.  The effect of overtaking behavior on unidirectional pedestrian flow , 2012 .

[9]  Dirk Helbing,et al.  Crowd disasters as systemic failures: analysis of the Love Parade disaster , 2012, EPJ Data Science.

[10]  Hiroshi Tsukaguchi,et al.  A new method for evaluation of level of service in pedestrian facilities , 1987 .

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

[12]  Fan Weicheng,et al.  An improved cross-correlation method for (digital) particle image velocimetry , 2001 .

[13]  William H. K. Lam,et al.  Variation of walking speeds on a unidirectional walkway and on a bidirectional stairway , 2006 .

[14]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[15]  W. C. Fan,et al.  Wavelet-based image denoising in (digital) particle image velocimetry , 2001, Signal Process..

[16]  B. D. Hankin,et al.  Passenger Flow in Subways , 1958 .

[17]  H. E. Fiedler,et al.  Limitation and improvement of PIV , 1993 .

[18]  Jun Zhang,et al.  Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions , 2011, 1102.4766.

[19]  Abishai Polus,et al.  Pedestrian Flow and Level of Service , 1983 .

[20]  Armin Seyfried,et al.  Microscopic insights into pedestrian motion through a bottleneck, resolving spatial and temporal variations , 2011, Collective Dynamics.

[21]  J. J. Wang,et al.  Limitation and improvement of PIV: Part I: Limitation of conventional techniques due to deformation of particle image patterns , 1993 .

[22]  A. Schadschneider,et al.  Enhanced Empirical Data for the Fundamental Diagram and the Flow Through Bottlenecks , 2008, 0810.1945.

[23]  Bernhard Steffen,et al.  New Insights into Pedestrian Flow Through Bottlenecks , 2009, Transp. Sci..

[24]  T. Roesgen,et al.  Optimal subpixel interpolation in particle image velocimetry , 2003 .

[25]  Mao-Bin Hu,et al.  Pedestrian flow dynamics in a lattice gas model coupled with an evolutionary game. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Dirk Helbing,et al.  From Crowd Dynamics to Crowd Safety: a Video-Based Analysis , 2008, Adv. Complex Syst..

[27]  Frederick M. Burkle,et al.  Cambodian Bon Om Touk Stampede Highlights Preventable Tragedy , 2012, Prehospital and Disaster Medicine.

[28]  Jun Zhang,et al.  Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing , 2009 .

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

[30]  Wenguo Weng,et al.  Empirical study of crowd behavior during a real mass event , 2012 .