Multi-agent-based human cognition simulation of Situation-aware earthquake emergency evacuation

The salvation of at-risk people from urban areas with earthquake prognosis or evacuation is one of the important activities of rescue teams. Due to the roles of different groups of individuals (crisis managers, pedestrians, and vehicles) with their special spatial cognition, as well as the various characteristics of transportation infrastructure, multi-agent-based modeling is an effective way of situation-aware simulation. The complexity and dynamic nature of emergencies are owing to the interactions between agents, evacuation environment, and multiple risk levels. This paper aims to develop a new approach to infer different situations before the occurrence of an earth-quake based on human spatial cognition with an emphasis on the routing process. This study proposed a situation-aware evacuation framework using multi-agent modeling to examine the agents (pedestrians, vehicles, and controlling agents), their behaviors, and agent-agent and agent-environment interactions. The initial routing was performed using the A* search algorithm, then route correction towards safe zones as action and decision-making on the part of the agent was done using the Bayesian inference method. The study area was District 6 of the Tehran metropolis, which is located very near active faults. The results were assessed under four different scenarios according to levels of agent cognition and access to environmental situation information. The research findings indicated that evacuation time in a state where agents are aware of the environmental situation and have sufficient knowledge was significantly less compared to other scenarios, which can contribute immensely to saving people's lives and reducing human casualties in emergencies and crises.