Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures

TL;DR

This paper introduces a cognitive agent-based simulation model that predicts and visualizes evacuation trajectories during disasters, aiding in the design of more effective evacuation procedures and guidance systems.

Contribution

It presents a novel agent-wayfinding framework grounded in human cognition and information theory, capable of modeling decision-making under uncertainty during evacuations.

Findings

Simulation tool visualizes evacuation trajectories and times

Framework fuses multiple dynamic information sources

Enables scenario recreation for procedure improvement

Abstract

In the event of a disaster, saving human lives is of utmost importance. For developing proper evacuation procedures and guidance systems, behavioural data on how people respond during panic and stress is crucial. In the absence of real human data on building evacuation, there is a need for a crowd simulator to model egress and decision-making under uncertainty. In this paper, we propose an agent-based simulation tool, which is grounded in human cognition and decision-making, for evaluating and improving the effectiveness of building evacuation procedures and guidance systems during a disaster. Specifically, we propose a predictive agent-wayfinding framework based on information theory that is applied at intersections with variable route choices where it fuses N dynamic information sources. The proposed framework can be used to visualize trajectories and prediction results (i.e., total evacuation time, number of people evacuated) for different combinations of reinforcing or contradicting information sources (i.e., signage, crowd flow, familiarity, and spatial layout). This tool can enable designers to recreate various disaster scenarios and generate simulation data for improving the evacuation procedures and existing guidance systems.