Real-time people movement estimation in large disasters from several kinds of mobile phone data

Recently, an understanding of mass movement in urban areas immediately after large disasters, such as the Great East Japan Earthquake (GEJE), has been needed. In particular, mobile phone data is available as time-varying data. However, much more detailed movement that is based on network flow instead of aggregated data is needed for appropriate rescue on a real-time basis. Hence, our research aims to estimate real-time human movement during large disasters from several kinds of mobile phone data. In this paper, we simulate the movement of people in the Tokyo metropolitan area in a large disaster situation and obtain several kinds of fragmentary movement observation data from mobile phones. Our approach is to use data assimilation techniques combining with simulation of population movement and observation data. The experimental results confirm that the improvement in accuracy depends on the observation data quality using sensitivity analysis and data processing speed to satisfy each condition for real-time estimation.