C03

Research Abstracts on Spatial Information ScienceCSIS DAYS 2015

CityMomentum: An Online Approach for Crowd Behavior Prediction at a Citywide Level
1Zipei Fan1, Xuan Song1, Ryosuke Shibasaki1 and Ryutaro Adachi2
Center for Spatial Information Science, The University of Tokyo, 2ZENRIN DataCom Co., Ltd.
Email: <fanzipei@iis.u-tokyo.ac.jp>(1) Introduction: Human movements are difficult topredict, especially, when we consider rare behaviors
that deviate from normal daily routines. By tracing
the behavior of a person over a long period, we canmodel their daily routines and predict periodical
behaviors, whereas rare behaviors, such as
participating in the New Year’s Eve countdown, canhardly be predicted readily and thus they have usually
been treated as outliers of the daily routines in mostexisting studies. However, for scenarios such as
emergency management or intelligent traffic
regulation, we are more interested in rare behaviors
than daily routines. Using human mobility Big Data,the rare behavior of each individual in a social crowd
is no longer rare and thus it may be predicted when
we analyze the crowd behavior at a citywide level.
(2) Methodology: In this study, instead of predicting
movement based on daily routines, we make short-term
predictions based on the recent movement observations.
We propose a novel model called CityMomentum as apredicting-by-clustering framework for sampling
future movement using a mixture of multiple random
Markov chains, each of which is a Naive Movement
Predictive model trained with the movements of thesubjects that belong to each cluster.
(3) Data: To address the problem of real-world humanmobility and to develop innovative applications that
may allow governments to accelerate the promotion ofsmart cities, we collected an anonymous GPS log
dataset from about 1.6 million real mobile-phone users
in Japan over a three-year period (August 1, 2010 toJuly 31, 2013). The dataset stored about 30 billion GPS
records with a total file size exceeding 1.5 terabytes
using a Hadoop cluster. The data were collected by amobile operator and a private company under anagreement with the mobile phone users. By default, thepositioning function in mobile phones is activated anduploaded about every 5 min. However, data acquisition
is not stable and robust due to several factors, such as
the loss of signal or battery power. In addition, thepositioning function is also turned off automaticallywhen no motion is detected from the mobile phone (e.g.
the phone is placed evenly on the table). The
characteristics of the dataset also cause handlingproblems, so we designed the CityMomentum modelto address these challenges.
(4) Results: We apply our approach to a big mobile phone
GPS log dataset and predict the short-term future
movements, especially during the Comiket 80 andNew Year’s Eve celebration. We evaluate ourprediction by a Earth Mover Distance (EMD) based
metric, and show our approach accurately predicts thecrowd behavior during the rare crowd events, whichmakes an early crowd event warning and regulation
possible in the emergent situations.
(5) Future steps: We note several limitations of our work.
First, a more sophisticated preprocessing will beincluded in our further research. Moreover, we predict
future movements based on the current transition
pattern, but it may deviate from the movements inreality if the transition pattern changes suddenly.

Figure 1: Predicted sources of visitors to C80 at 1 h later and predicted 1-h-later locations of the visitors at C80(in the top row) and the actual sources of and 1-h-later locations of visitors in reality (in the bottom row)

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