A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data

Authors

  • Xuan Song The University of Tokyo
  • Quanshi Zhang The University of Tokyo
  • Yoshihide Sekimoto The University of Tokyo
  • Ryosuke Shibasaki The University of Tokyo
  • Nicholas Jing Yuan Microsoft Research
  • Xing Xie Microsoft Research

DOI:

https://doi.org/10.1609/aaai.v29i1.9237

Keywords:

human mobility, big data, disaster informatics

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 becomethe critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniquenessof 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 morepredictable and can be easier simulated than previously thought.

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Published

2015-02-10

How to Cite

Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Yuan, N. J., & Xie, X. (2015). A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9237

Issue

Section

Computational Sustainability and Artificial Intelligence