DEFINITION OF FUZZY CONDITIONS AND ANALYSIS OF EXISTING SOLUTIONS TO THE PROBLEM OF EVACUATION IN EMERGENCY SITUATIONS
Abstract
Quantification in collective behavior and decision-making in fuzzy conditions is crucial to
ensure the health and safety of the population. The task of modeling and predicting behavior in
fuzzy conditions, as is known, has increased complexity due to a large number of factors from
which an NP-complete multi-criteria problem is formed. There is a difficulty in quantifying the impact of fuzzy factors using a mathematical model. In this regard, the paper proposes a stochastic
model of human decision-making to describe the empirical behavior of subjects in an experiment
simulating an emergency scenario. The developed fuzzy model combines fuzzy logic into a
conventional model of social behavior. Unlike existing models and applications, this approach
uses fuzzy sets and membership functions to describe the evacuation process in an emergency
situation. The purpose of this work is to define fuzzy rules and analyze existing solutions. The scientific
novelty lies in the formation of a set of factors that form fuzzy rules for making dynamic
decisions. The problem statement in this paper is as follows: to form a set of factors affecting the
behavior of pedestrians, which are modeled as fuzzy input data. The practical value of the work
lies in the creation of a new set of fuzzy rules that allows them to be used in the evacuation algorithm
for the effective solution of the task. The fundamental difference from the known approaches
is in the application of a new set of fuzzy rules, which contains factors: perception, intention, attitude.
To implement the proposed model, the process of social behavior during evacuation, independent
variables are determined. These variables include measurements related to social factors,
in other words, the behavior of individual subjects and individual small groups, which are fundamental
at an early stage of evacuation.
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