C01

Research Abstracts on Spatial Information Science
CSIS DAYS 2015

Next Generation Urban Emergency Management:
When Human Emergency Mobility Modeling Meets Big DataXuan SongCenter for Spatial Information Science, The University of Tokyo
Email: <songxuan@csis.u-tokyo.ac.jp> Web: <http://shiba.iis.u-tokyo.ac.jp/song/>(1) Motivation: The frequency and intensity of natural
disasters has significantly increased over past
decades and this trend is predicted to continue.
Facing these possible and unexpected disasters,urban emergency management has become a big
problem for governments across the world. To planeffective humanitarian relief, transportation
scheduling, disaster management and long-termsocietal reconstruction, modeling, understanding
and accurately predicting human emergency
behavior and mobility will play a most importantrole for the next generation of urban emergency
management. This research aims to understand whatbasic laws govern human behavior and mobility
following emergency events by mining big andheterogeneous data, to develop deep and powerfulmodels for human emergency mobility prediction
and simulation.
(2) Approach: In this study, we collect big andheterogeneous data, and develop several deep andpowerful models (Song et al., 2015, 2014, 2013) for
human emergency mobility prediction andsimulation, and implement the key modules ofintelligent system for next-generation urban
emergency management.
(3) Significance: It is well known that hurricanes,
earthquakes, and other natural disasters cause
immense physical destruction, loss of life andproperty around the world. This research is of greatsignificance in the frontier big data application field,
and has the potential for enormous societal andeconomic impacts by minimizing the economic loss,transportation disorders, and business closures after
major disasters or emergency events (e.g. traffic
accident, fire, extreme weather, etc.).
(4) Novelty and characteristics:
・ Big Data and heterogeneous data source: Our
research will be built on big and heterogeneous data
sources, e.g. GPS trajectory data (millions of users),
natural disaster data, urban traffic accident data,
CDR data, news reporting data, emergency events
data, etc. To the best of our knowledge, there is noexisting research has been carried out to understandhuman mobility at such kind of data size.
・ Deep models of human behavior and mobility:
Our developed models will discover deep
knowledge of human emergency behavior andmobility. To the best of our knowledge, this is thefirst attempt that applies deep learning approaches

for human mobility modeling. Furthermore, thepredictive or simulation models are general models
that can be applied to all people, in all countries
and for all disasters.
(5) Acknowledgement: This work is supported byCenter for Spatial Information Science, The
University of Tokyo (Joint Research No. 547).
(6) Reference:
X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N.
Yuan, X. Xie (2015) A Simulator of Human
Emergency
Mobility
following
Disasters:
Knowledge Transfer from Big Disaster Data, Proc.
of Twenty-Ninth AAAI Conference on Artificial
Intelligence (AAAI), 730-736.
X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki,(2014) Prediction of Human Emergency Behavior
and their Mobility following Large-scale Disaster,Proc. of SIGKDD conference on Knowledge
Discovery and Data Mining (KDD 2014), 5-14.
X. Song, Q. Zhang, Y. Sekimoto, T. Horanont, S.
Ueyama, R. Shibasaki (2013) Intelligent System for
Human Behavior Analysis and Reasoning Following
Large-Scale Disasters, IEEE Intelligent Systems, 28(4), 35-42.

Figure 1: Deep Models and Big Data for Human
Mobility Understanding- 33 -