A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques

TL;DR

This paper presents an advanced simulation model for pedestrian evacuation that integrates cellular automata with fuzzy logic, KNN, and statistical methods to predict evacuation times and behaviors under different scenarios.

Contribution

The novel integration of cellular automata with fuzzy logic, KNN, and statistical equations for more accurate pedestrian evacuation simulation.

Findings

Model accurately predicts evacuation times in various scenarios

Simulation captures diverse emergency behaviors

Effective in different building interior layouts

Abstract

This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata(CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his or her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements all have great impacts on the evacuation process. Several experiments were carried out to verify the performance of the model in different emergency scenarios. The results show that the proposed model can predict the evacuation time and emergency behavior in various types of building interiors and pedestrian distributions. The research provides a good reference to the design of building evacuation systems.