Concept of a Decision-Based Pedestrian Model
Keywords:modelling, perception, decision-based model, distance-to-collision, shdv model
AbstractWe develop a decision-based model for pedestrian dynamics which is an extension of the Stochastic Headway Distance Velocity (SHDV) model for single-file motion to two dimensions. The model is discrete in time, but continuous in space. It combines perception, anticipation and decision-making with the simplicity and stochasticity that are characteristic for cellular automaton models. The basic concept is discussed and preliminary results show that the model yield realistic trajectories and fundamental diagrams.
A. Schadschneider, W. Klingsch, H. Klüpfel et al., “Evacuation dynamics: empirical results, modelling and applications,“ in Encyclopedia of Complexity and Systems Science, R.A. Meyers, Ed. New York: Springer, 2009, pp. 3142-3176.
A. Schadschneider, M. Chraibi, A. Seyfried et al., „Pedestrian dynamics – from empirical results to modelling”, to appear in Crowd Dynamics, Volume 1 – Theory, Models, and Safety Problems, L. Gibelli and N. Bellomo, Ed. Springer.
D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Phys. Rev. E, vol. 51, pp. 4282-4286, 1995.
M. Fukui and Y. Ishibashi, “Jamming transition in cellular automaton models for pedestrians,” J. Phys. Soc. Japan, vol. 68, pp. 3738-3739, 1999.
V. Blue and J. Adler, “Cellular automata microsimulation of bidirectional pedestrian flow,” Transp. Res. Rec., vol. 1678, pp. 135-141, 2000.
C. Burstedde, K. Klauck, A. Schadschneider and J. Zittartz, „Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Phys. A, vol. 295, pp. 507-525, 2001.
K. Yamamoto, S. Kokubo and K. Nishinari, „Simulation for pedestrian dynamics by real-coded cellular automata (RCA),” Phys. A, vol. 379, pp. 645-660, 2001.
I. Karamouzas and M. Overmars, „A velocity-based approach for simulating human collision avoidance,” in Intelligent Virtual Agents, Springer, 2010, pp. 180-186.
G. Baglietto and D.R. Parisi, „Continuous-space automaton model for pedestrian dynamics,” Phys. Rev. E, vol. 83, no. 056117, 2011.
Z.-M. Fang, W. Song, X. Liu et al., „A continuous distance model (CDM) for single-file pedestrian movement considering step-frequency and length,” Phys. A, vol. 391, pp. 307-316, 2012.
M.J. Seitz and G. Köster, „Natural discretization of pedestrian movement in continuous space,” Phys. Rev. E, vol. 86, no. 046108, 2012.
C. Eilhardt and A. Schadschneider, „Stochastic headway dependent velocity model for 1d pedestrian dynamics at high densities,” Transp. Res. Proc., vol. 2, pp. 400-405, 2014.
W. Kang and Y. Han, „A simple and realistic pedestrian model for crowd simulation and application,” arXiv:1708.03080, 2017.
S. Nowak and A. Schadschneider, “Quantitative analysis of pedestrian counterflow in a cellular automaton model,” Phys. Rev. E, vol. 85, no. 066128, 2012.
Y. Suma, D. Yanagisawa and K. Nishinari, “Anticipation effect in pedestrian dynamics: modelling and experiments,” Phys. A, vol. 391, pp. 248-263, 2012.
G. Antonini, M. Bierlaire and M. Weber, “Discrete choice models of pedestrian walking behavior,” Transp. Res. B, vol. 40, pp. 667-687, 2006.
M. Moussaïd, D. Helbing and G. Theraulaz, “How simple rules determine pedestrian behavior and crowd disasters,” PNAS, vol. 108, pp. 6884-6888, 2011.
M. Zhou, H. Dong, F.-Y. Wang et al., “Modeling and simulation of pedestrian dynamics behavior based on a fuzzy logic approach”, Information Sciences, vol. 360, pp. 112-130, 2016.
A. Johansson, “Constant-net-time headway as a key mechanism behind pedestrian flow,” Phys. Rev. E, vol. 80, no. 026120, 2009.
P. Degond, C. Appert-Rolland, M. Moussaïd et al., “A hierarchy of heuristic-based models of crowd dynamics,” J. Stat. Phys., vol. 152, pp. 1033-068, 2013.
M. Asano and T. Iryo and M. Kuwahara, “Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour,” Transp. Res. C, vol. 18, pp. 842-855, 2010.
R. Barlovic, L. Santen, A. Schadschneider and M. Schreckenberg, “Metastable states in cellular automata for traffic flow,“ Europ. Phys. J. B, vol. 5, pp. 793-800, 1998.
How to Cite
Copyright (c) 2020 Cornelia von Krüchten, Andreas Schadschneider
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to Collective Dynamics agree to publish their articles under the Creative Commons Attribution 4.0 license.
This license allows:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Authors retain copyright of their work. They are permitted and encouraged to post items submitted to Collective Dynamics on personal or institutional websites and repositories, prior to and after publication (while providing the bibliographic details of that publication).