DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from Big and Heterogeneous Data

作者：Song, Xuan^*; Shibasaki, Ryosuke; Yuan, Nicholos Jing; Xie, Xing; Li, Tao; Adachi, Ryutaro

来源：ACM Transactions on Information Systems, 2017, 35(4): 41.

DOI：10.1145/3057280

• Information Systems
• Human mobility
• disaster informatics
• urban computing
• spatiotemporal data mining

The frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Hence, understanding and predicting human evacuation behavior and mobility will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, existing models are shallow models, and it is difficult to apply them for understanding the "deep knowledge" of human mobility. Therefore, in this study, we collect big and heterogeneous data (e.g., GPS records of 1.6million users over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data), and we build an intelligent system, namely, DeepMob, for understanding and predicting human evacuation behavior and mobility following different types of natural disasters. The key component of DeepMob is based on a deep learning architecture that aims to understand the basic laws that govern human behavior and mobility following natural disasters, from big and heterogeneous data. Furthermore, based on the deep learning model, DeepMob can accurately predict or simulate a person's future evacuation behaviors or evacuation routes under different disaster conditions. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human mobility following disasters may be predicted and simulated more easily than previously thought.

• 出版日期2017-8
• 单位南京邮电大学