Simulation of Wheelchair Movements in Crowd Using Fine Grid Cellular Automata

TL;DR

This paper presents a modified fine grid cellular automata model for simulating wheelchair movements within crowds, accurately capturing their effects on crowd dynamics and safety, validated against empirical data.

Contribution

The study introduces a novel modification to cellular automata models to incorporate wheelchairs of various sizes and speeds in crowd simulations.

Findings

The model accurately reproduces the slowdown effect of wheelchairs.

Simulation results match empirical crowd behavior data.

Density-speed relationships are effectively modeled.

Abstract

Crowd simulation models are used to assess the performance and safety of crowd systems. In some systems, wheelchairs and other moving objects are present in the crowd. The different size and speed of the wheelchairs could significantly change the behavior and dynamics of the crowd. In order to minimize the risks of overcrowding and other types of accidents, it is important to properly model the wheelchairs and their interactions with pedestrians and the environment. Cellular automata are extensively utilized in crowd modeling because of their simple and fast algorithms. Fine grid cellular automata model uses small cells in which moving entities (pedestrians, wheelchairs, cars and etc.) occupy several cells. The entities could have different sizes, shapes, and speeds. In this article, fine grid cellular automata model has been modified to allow building crowd simulation models with different ratios of wheelchairs that could be of different sizes and speed profiles. A scenario of a walkway has been used to evaluate the model. The slow down effect of the slower wheelchairs has been properly reproduced in the results which also match empirical data. Density-speed graphs are also compared to crowds comprising of only pedestrians.