Cellular Automaton Approach to Pedestrian Dynamics - Theory

By Andreas Schadschneider. 2001

300 citations recorded by the source corpus.

We present a 2-dimensional cellular automaton model for the simulation of pedestrian dynamics. The model is extremely efficient and allows simulations of large crowds faster than real time since it includes only nearest-neighbour interactions. Nevertheless it is able to reproduce collective effects and self-organization encountered in pedestrian dynamics. This is achieved by introducing a so-called floor field which mediates the long-range interactions between the pedestrians. This field modifies the transition rates to neighbouring cells. It has its own dynamics (diffusion and decay) and can be changed by the motion of the pedestrians. Therefore the model uses an idea similar to chemotaxis, but with pedestrians following a virtual rather than a chemical trace.

Subfields: Building and Construction, Computational Theory and Mathematics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/dadc9a2c0d6de23d077e9680aa649ca64fc05cfc

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In Building and Construction

In Computational Theory and Mathematics

In Ocean Engineering

Citations in the corpus

Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data (2007, unknown): “nt ows and stop-and-go trac emerging at extreme densities [11]. However, most recent work is dedicated to microscopic simulation models such as the social force model [8{10,14,38], cellular automata [20,21,30], particle hopping models [27], and others, e.g. multi-agent approaches [1,37]. While some ”
A synthetic-vision based steering approach for crowd simulation (2010, methodology): “Cellular-automaton approaches [Schadschneider 2001] are used to simulate evacuation scenarios for large crowds: the discrete aspect of resulting trajectories prevent their use for Computer Animation applications.”
You'll never walk alone: Modeling social behavior for multi-target tracking (2009, background): “In yet another branch, cellular automata are used, which discretize the space and select the next desired direction from a preference matrix, e.g. [21].”
Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings (2010, methodology): “Applications include simulation [2–4], computer graphics [5, 6], and, in the last few years, also Computer Vision [7–14].”
Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution (2005, methodology): “Particular models of this category are described in Schadschneider [1]; Blue and Adler [2]; Dijkstra, Jesurun, and Timmermans [3]; Kessel et al.”