AAAI Publications, Twenty-Eighth AAAI Conference on Artificial Intelligence

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Intelligent System for Urban Emergency Management during Large-Scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki

Last modified: 2014-06-20


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, urban emergency management has become the especially important issue for the whole governments around the world.In this paper, we present a novel intelligent system for urban emergency management during the large-scale disasters. The proposed systemstores and manages the global positioning system (GPS) records from mobile devices used by approximately 1.6 million people throughout Japan over one year. By mining and analyzing population movements after the Great East Japan Earthquake, our system can automatically learn a probabilistic model to better understand and simulate human mobility during the emergency situations. Based on the learning model, population mobility in various urban areas impacted by the earthquake throughout Japan can be automatically simulated or predicted. On the basis of such kind of system, it is easy for us to find some new features or population mobility patterns after the recent and unprecedented composite disasters, which are likely to provide valuable experience and play a vital role for future disaster management worldwide.


Human Mobililty; Urban Computing; Emergency Mangement

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