Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures
This work addresses the need for better disaster response planning for building designers and emergency managers, though it is incremental as it builds on existing agent-based simulation methods.
The paper tackles the problem of improving building evacuation procedures during disasters by developing an agent-based simulation tool grounded in human cognition and decision-making, which models egress and decision-making under uncertainty to evaluate and enhance guidance systems, with results including visualization of trajectories and prediction of total evacuation time and number of people evacuated.
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.