Pedestrian traffic: on the quickest path

When a large group of pedestrians moves around a corner, most pedestrians do not follow the shortest path, which is to stay as close as possible to the inner wall, but try to minimize the travel time. For this they accept to move on a longer path with some distance to the corner, to avoid large densities and by this succeed in maintaining a comparatively high speed. In many models of pedestrian dynamics the basic rule of motion is often either 'move as far as possible toward the destination' or—reformulated—'of all coordinates accessible in this time step move to the one with the smallest distance to the destination'. On top of this rule modifications are placed to make the motion more realistic. These modifications usually focus on local behavior and neglect long-ranged effects. Compared to real pedestrians this leads to agents in a simulation valuing the shortest path a lot better than the quickest. So, in a situation such as the movement of a large crowd around a corner, one needs an additional element in a model of pedestrian dynamics that makes the agents deviate from the rule of the shortest path. In this work it is shown how this can be achieved by using a flood fill dynamic potential field method, where during the filling process the value of a field cell is not increased by 1, but by a larger value, if it is occupied by an agent. This idea may be an obvious one: however, the tricky part—and therefore in a strict sense the contribution of this work—is (a) to minimize unrealistic artifacts, as naive flood fill metrics deviate considerably from the Euclidean metric and in this respect yield large errors, (b) do this with limited computational effort and (c) keep agents' movement at very low densities unaltered.

[1]  Serge P. Hoogendoorn,et al.  DYNAMIC USER-OPTIMAL ASSIGNMENT IN CONTINUOUS TIME AND SPACE , 2004 .

[2]  E. F. Codd,et al.  Cellular automata , 1968 .

[3]  H. Euler,et al.  Über die Streuung von Licht an Licht nach der Diracschen Theorie , 1935, Naturwissenschaften.

[4]  Samira El Yacoubi,et al.  Cellular Automata, 7th International Conference on Cellular Automata, for Research and Industry, ACRI 2006, Perpignan, France, September 20-23, 2006, Proceedings , 2006, ACRI.

[5]  Dirk Helbing,et al.  The social force pedestrian model applied to real life scenarios , 2003 .

[6]  Michael Schreckenberg,et al.  Traffic and Granular Flow’01 , 2003 .

[7]  Serge P. Hoogendoorn,et al.  Pedestrian route-choice and activity scheduling theory and models , 2004 .

[8]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[9]  J A Sethian,et al.  Computing geodesic paths on manifolds. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Tobias Kretz,et al.  Pedestrian Traffic - Simulation and Experiments , 2007 .

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

[12]  Michael Schreckenberg,et al.  F.A.S.T. - Floor field- and Agent-based Simulation Tool , 2006, ArXiv.

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

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

[15]  Peter Vortisch,et al.  Comparison of Various Methods for the Calculation of the Distance Potential Field , 2008, ArXiv.

[16]  Teresa L. Young,et al.  The F.A.S.T. Model , 2007 .

[17]  Hubert Klüpfel,et al.  Evacuation Dynamics: Empirical Results, Modeling and Applications , 2009, Encyclopedia of Complexity and Systems Science.

[18]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .

[19]  A. Schadschneider Cellular Automaton Approach to Pedestrian Dynamics - Theory , 2001, cond-mat/0112117.

[20]  Hans Euler Über die Streuung von Licht an Licht nach der Diracschen Theorie , 1936 .

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

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

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

[24]  Michael Schreckenberg,et al.  Moore and more and symmetry , 2008, ArXiv.

[25]  G. Swaminathan Robot Motion Planning , 2006 .

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

[27]  John J. Fruin,et al.  Pedestrian planning and design , 1971 .

[28]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[29]  Peter Vortisch,et al.  A DYNAMIC TRAFFIC ASSIGNMENT METHOD FOR PLANNING AND TELEMATIC APPLICATIONS , 2000 .

[30]  Oussama Khatib,et al.  The Potential Field Approach And Operational Space Formulation In Robot Control , 1986 .

[31]  Andreas Schadschneider Bionics-Inspired Cellular Automaton Model for Pedestrian Dynamics , 2003 .

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

[33]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[34]  T Fujiyama Collision avoidance of pedestrians on stairs , 2006 .

[35]  Andreas Schadschneider,et al.  Extended Floor Field CA Model for Evacuation Dynamics , 2004, IEICE Trans. Inf. Syst..

[36]  Colin Marc Henein Crowds are made of people: Human factors in microscopic crowd models , 2008 .

[37]  Ansgar Kirchner,et al.  Modellierung und statistische Physik biologischer und sozialer Systeme , 2002 .

[38]  R. Blandford,et al.  Fermat's principle, caustics, and the classification of gravitational lens images , 1986 .

[39]  Michael Schreckenberg,et al.  The F.A.S.T.-Model , 2006, ACRI.

[40]  Michael Schreckenberg,et al.  Counterflow Extension for the F.A.S.T.-Model , 2008, ACRI.

[41]  M. Schreckenberg,et al.  Experimental study of pedestrian counterflow in a corridor , 2006, cond-mat/0609691.

[42]  A. Millonig,et al.  A Navigation Algorithm for Pedestrian Simulation in Dynamic Environments , 2007 .