The stepping behavior analysis of pedestrians from different age groups via a single-file experiment

The stepping behavior of pedestrians with different age compositions in single-file experiment is investigated in this paper. The relation between step length, step width and stepping time are analyzed by using the step measurement method based on the calculation of curvature of the trajectory. The relations of velocity-step width, velocity-step length and velocity-stepping time for different age groups are discussed and compared with previous studies. Finally effects of pedestrian gender and height on stepping laws and fundamental diagrams are analyzed. The study is helpful for understanding pedestrian dynamics of movement. Meanwhile, it offers experimental data to develop a microscopic model of pedestrian movement by considering stepping behavior.

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

[2]  Timo Korhonen,et al.  FDS+Evac: Herding Behavior and Exit Selection , 2011 .

[3]  Li-Yun Dong,et al.  Self-organized phenomena of pedestrian counter flow in a channel under periodic boundary conditions , 2012 .

[4]  H. H. Madden.  Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data , 1976 .

[5]  Jun Zhang,et al.  Step styles of pedestrians at different densities , 2018 .

[6]  J. Pettré,et al.  Properties of pedestrians walking in line: fundamental diagrams. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Cécile Appert-Rolland,et al.  Properties of pedestrians walking in line. II. Stepping behavior. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[9]  Angel Garcimartín,et al.  Experimental proof of faster-is-slower in systems of frictional particles flowing through constrictions. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Shuchao Cao,et al.  Pedestrian dynamics in single-file movement of crowd with different age compositions. , 2016, Physical review. E.

[11]  Daniel R. Parisi,et al.  Experimental evidence of the "Faster is Slower" effect in the evacuation of ants , 2012 .

[12]  Dirk Helbing,et al.  Self-Organized Network Flows , 2007 .

[13]  Ulrich Weidmann,et al.  Transporttechnik der Fussgänger: Transporttechnische Eigenschaften des Fussgängerverkehrs, Literaturauswertung , 1992 .

[14]  Manoj Srinivasan,et al.  Computer optimization of a minimal biped model discovers walking and running , 2006, Nature.

[15]  N. L. Svensson,et al.  The influence of surface slope on human gait characteristics: a study of urban pedestrians walking on an inclined surface. , 1996, Ergonomics.

[16]  M. Burger,et al.  Continuous limit of a crowd motion and herding model: Analysis and numerical simulations , 2011 .

[17]  Julien Pettré,et al.  A step‐by‐step modeling, analysis and annotation of locomotion , 2011, Comput. Animat. Virtual Worlds.

[18]  David J. Low,et al.  Following the crowd , 2000 .

[19]  Angel Garcimartín,et al.  The Conference in Pedestrian and Evacuation Dynamics 2014 ( PED 2014 ) Experimental evidence of the “ Faster Is Slower ” effect , 2014 .

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

[21]  Andreas Schadschneider,et al.  Automatic Extraction of Pedestrian Trajectories from Video Recordings , 2010 .

[22]  John R. Rebula,et al.  Influence of contextual task constraints on preferred stride parameters and their variabilities during human walking. , 2015, Medical engineering & physics.

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

[24]  Jun Li,et al.  Approximation of reachable sets for coherently controlled open quantum systems: Application to quantum state engineering , 2014, 1412.4146.

[25]  Armin Seyfried,et al.  Collecting pedestrian trajectories , 2013, Neurocomputing.

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

[27]  Felix Dietrich,et al.  The effect of stepping on pedestrian trajectories , 2015 .

[28]  Jean-Paul Laumond,et al.  On the nonholonomic nature of human locomotion , 2008, Auton. Robots.

[29]  Gerta Köster,et al.  Natural discretization of pedestrian movement in continuous space. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.