An optimization-based overtaking model for unidirectional pedestrian flow

Abstract We propose an optimization-based model for simulating the overtaking behaviour in the unidirectional pedestrian flow. A ‘visual area’ is introduced so that agents could receive the information regarding their surroundings and react by choosing one of three options: to move straight on, to dodge to the left, or to dodge to the right. And a side preference of each pedestrian for evading and overtaking is implemented based on traffic ‘social norms’. The model was validated by reproducing the experimentally obtained pedestrian flow patterns. The effects of the initial pedestrian formation on overtaking behaviour and the evacuation time have been analysed in different geometries. The results show that pedestrian flow patterns after overtaking are obviously influenced by both the initial positions and density of the slow pedestrians in the front. Phase changes of pedestrian formation are observed in both experiment and simulations. On the other hand, for sparse pedestrian crowds, the egress time of the fast individuals is mainly impacted by the horizontal distance between the initial positions of the slow pedestrians in the front, especially in the geometry with a bottleneck.

[1]  Stefania Bandini,et al.  Mobility analysis of the aged pedestrians by experiment and simulation , 2014, Pattern Recognit. Lett..

[2]  Nagel.  Particle hopping models and traffic flow theory. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Takayuki Kanda,et al.  A Microscopic “Social Norm” Model to Obtain Realistic Macroscopic Velocity and Density Pedestrian Distributions , 2012, PloS one.

[4]  Edward Lester,et al.  Modelling contra-flow in crowd dynamics DEM simulation. , 2009 .

[5]  H. Löwen,et al.  Lane formation in colloidal mixtures driven by an external field. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  陈姚,et al.  A new collision avoidance model for pedestrian dynamics , 2015 .

[7]  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.

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

[9]  Harri Ehtamo,et al.  Integration of an agent based evacuation simulation and state-of-the-art fire simulation , 2007 .

[10]  Wenjian Yu,et al.  Modeling crowd turbulence by many-particle simulations. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  M. Schreckenberg,et al.  Experimental study of pedestrian flow through a bottleneck , 2006, physics/0610077.

[12]  Dirk Helbing,et al.  Patient and impatient pedestrians in a spatial game for egress congestion. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  W. Weng,et al.  Cellular automaton simulation of pedestrian counter flow with different walk velocities. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Dirk Helbing,et al.  Collective phenomena and states in traffic and self-driven many-particle systems , 2004 .

[15]  Harri Ehtamo,et al.  Counterflow model for agent-based simulation of crowd dynamics , 2012 .

[16]  Takamasa Iryo,et al.  Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour , 2010 .

[17]  Weiguo Song,et al.  Modeling pedestrian evacuation with guiders based on a multi-grid model , 2016 .

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

[19]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[20]  D. Helbing,et al.  Analytical approach to continuous and intermittent bottleneck flows. , 2006, Physical review letters.

[21]  Serge P. Hoogendoorn,et al.  Experimental Research of Pedestrian Walking Behavior , 2003 .

[22]  Taewan Kim,et al.  Modeling lane formation in pedestrian counter flow and its effect on capacity , 2016 .

[23]  Frank Biess.  The Concept of Panic , 2014 .

[24]  Tony White,et al.  Macroscopic effects of microscopic forces between agents in crowd models , 2007 .

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

[26]  Jonathan D. Nelson,et al.  Simple Heuristics and the Modelling of Crowd Behaviours , 2014 .

[27]  Haiying Li,et al.  Social force models for pedestrian traffic – state of the art , 2018 .

[28]  Daniel R. Parisi,et al.  A modification of the Social Force Model can reproduce experimental data of pedestrian flows in normal conditions , 2009 .

[29]  Michael Batty,et al.  Predicting where we walk , 1997, Nature.

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

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

[32]  Eric W. Marchant,et al.  A computer model for the evacuation of large building populations , 1995 .

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