A simulator of human emergency mobility following disasters: Knowledge transfer from big disaster data

Abstract

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding and simulating of human emergency mobility following disasters will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniqueness of various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. Hence, in this paper, we collect big and heterogeneous data (e.g. 1.6 million users' GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop a general model of human emergency mobility for generating and simulating large amount of human emergency movements. The experimental results and validations demonstrate the efficiency of our simulation model, and suggest that human mobility following disasters may be significantly more predictable and can be easier simulated than previously thought.