There are several advantages of applying multi-agent systems to study mass evacuations like those triggered by mega tsunamis. Finally, demonstrative applications that highlight the need for including sub-meter details in the environment, different modes of evacuation and behavioural differences are presented.
A scalable high-performance computing (HPC) extension to address the high-computational demand of complex agents and high-resolution model of environment is briefly explained. The results of tuning and validating of constituent functions for pedestrian-pedestrian, car-car and car-pedestrian interactions are presented. The agents, which mimic the heterogeneous crowd of evacuees, are composed of different combinations of basic constituent functions for modelling interaction with each other and environment, decision-making, etc. This hybrid of raster and vector data structures enables modelling large domain in a scalable manner. The environment of the multi-agent system, which mimics the physical world of evacuees, is modelled as a hybrid of a high-resolution grid and a graph connecting traversable spaces. A brief description of the multi-agent system is provided using a mathematical framework as means to easily and unambiguously refer to the main components of the system. Considering the large number of lives in fatal danger, instead of widely used simple agents in 1D networks, we use a high-resolution model of environment and complex agents so that wide range of influencing factors can be taken into account. The main objective is to quantitatively evaluate various strategies to accelerate evacuation in case of a tsunami with a short arrival time, taking most influential factors into account. This chapter presents an application of multi-agent systems to simulate tsunami-triggered mass evacuations of large urban areas.
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