Abstract

Evacuation plans in seacoast areas are essential for conducting people to secure zones in a timely manner. Typically, evacuation plans are based on the experience of previous evacuation drills, which are expensive processes that require coordination, planning and the collaboration of different institutions and people. During evacuation drills it is difficult to obtain all the data required to analyze the situation and additionally, it is difficult to detect all possible threatening situations. Computer simulations can be used to run evacuation models for evaluating different evacuation scenarios. However, developing realistic simulations is a complex task. Moreover, large simulation models considering many thousands of people demand a high computational cost and thereby, the simulation of different evacuation plans can become a highly time-consuming task. In this work, we present an approach to model and simulate the behavior of people in mass evacuations of seacoast areas. Our proposal aims to improve the computational efficiency of the calculations performed without compromising the quality of results by means of parallel computing. The simulation model divides the geographic area in cells of fixed sizes. Then, to reduce the amount of calculations performed in each simulation timestep, for each simulated agent we compute a mobility model by considering only the agents placed in the closest neighboring cells. The proposed simulation model achieves realistic results by combining geographic data, public census data, the density of the population, the surrounding view of each person and disaggregation by age groups. This reduces the error in decision making and allows a proper estimation of the distance of groups of people that cannot arrive at safe areas. The respective simulator has been implemented using agent-based programming in C++ and OpenMP. The simulation model was evaluated by performing experimentation on actual data collected from the Chilean cities of Iquique and Vina del Mar, and the city of Kesennuma in Japan.