Citation

Porzycki J, Wąs J. Safety Sci. 2023; 167: e106266.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ssci.2023.106266

PMID

unavailable

Abstract

On the basis of data-driven methodology, especially velocity correlations of pedestrians moving in a crowd, we have proposed a new model of pedestrian dynamics with an easy-adjustable space discretization. The model is based on Cellular Automata (CA) with an adaptive lattice and takes into account proxemics patterns among pedestrians. The proposed model uses agents located on the CA lattice, constructed using the concept of Floor Field (FF), namely, a set of gradient potential fields influencing the movement of agents. In the model, we have proposed three kinds of such fields: Static FF--responsible for navigation to agents' Points of Interest (POIs), Wall FF--a repulsive influence with obstacles, and Interplay FF--to model agents' volume and proxemics effects. The third field, which models mutual relations between pedestrians taking into account the rules of proxemics is an added value in the area of crowd modeling and simulation. During the creation of the proposed model, we have based on a set of experimental data provided by Jülich Supercomputing Centre and our previous analysis of spatial patterns and velocity correlations. Thus, the proposed model includes, among other things the most probable positions of the nearest neighbors in a crowd--including the shape of "the forbidden zone" around a pedestrian and the finding of rules explaining observed mutual spatial relations between neighbors in a crowd. In the described model the cell size is an adjustable parameter, with available values from 1 cm to 50 cm. It is possible with the usage of embodied agents which always occupy only one cell of Cellular Automata, actual pedestrian volume is modeled with the Interplay FF. An interesting property of the model is that changing the cell size does not involve changing the rules of the model, e.g. its transition function.

Language: en

Keywords

Cellular Automata; Crowd modeling; Data-driven modeling; Floor Field; Proxemics; Social distances