Application of bug navigation algorithms for large-scale agent-based evacuation modeling to support decision making

Abstract

The global growth of urbanization has caused an increase of wildfires that devastate communities at wildland-urban interfaces. Agent-based evacuation simulation can be a powerful tool for emergency decision-makers to estimate evacuation times during rapidly propagating wildfires. However, as these models increase in size and complexity for urban areas, computation costs rise significantly, making these models less suitable for emergency situations. Although advances in high-performance computing have partially addressed the long execution time associated with large-scale models, the overall computation costs are still high considering the required infrastructure and technical knowledge. Alternatively, we can simplify certain components of the models such that accuracy will not be compromised greatly while computation speed is increased significantly. The authors suggest using the bug navigation algorithms, popular in the field of robotics, for the navigation of pedestrians. In the interest of finding the best candidate bug algorithm, a performance evaluation framework is also introduced. To demonstrate applicability, the evacuation of the city of Iquique, Chile, is simulated using the proposed approach. The results show that the proposed model is successful in estimating the evacuee arrival times, while execution time is reduced by orders of magnitude without a need for powerful processing resources.