Abstract
With the aim of estimating the effectiveness of the measures taken to smoothen and speedup the evacuation process of a large urban area, in time critical events like tsunami, a multi agent based mass evacuation simulation software is being developed. Considering the fact that it involves large number of human casualties, moderately complex agents in 2D grid environments are implemented. A vision based autonomous navigation algorithm, which enables the agents to move through an urban environment and reach a far visible destination, is implemented. The navigation algorithm is verified comparing the simulated evacuation time and the paths taken by individual agents with those of theoretical. We adopted optimal reciprocal collision avoidance algorithm for collision avoidance and validated by comparing with filed observations reported in literature. A parallel computing extension is developed for studying mass evacuation of large areas; simulating millions of agents with vision based navigation and collision avoidance is computationally intensive. A high strong scalability is attained with two millions of agents, which indicates potential to simulate mass evacuation involving many millions of people.
