Recently, monitoring dynamic changes in people flow has become necessary, in order to mitigate secondary disasters following earthquakes, fires, or other major events, as well as to mitigate congestion at nodes in terminal stations. From the point of view of public facility managers, it is necessary to grasp the people flow comprehensively, for instance, in order to design safe and comfortable spaces and appropriate urban transport policies. In commercial fields of outdoor advertisement, price systems, which support an effective advertising activity, depending on the traffic volume of people for each location.
In technical terms, tracking mobile objects by GPS or PHS, tracking the number of people who are stationary by CCTV camera, tracking the number of passengers getting on and off according to the number of IC (integrated circuit) tickets through the automatic ticket gates, tracking the number of people who are stationary by the number of registered mobile phones at each base station, and tracking the hourly number of visitors to department stores enables us to measure people flow according to various dimensions.
However, the scope of many of these goes no further than data acquisition technology. Such research cannot be seen as infrastructure data that can aggregate the acquired data and provide an overview of the mass flow. This is true in terms of the comprehensive qualities including spatial/temporal accuracy, acquisition/process cost, and value to the user as a service.
Therefore, we start the “People Flow Project (PFLOW)” which overviews data process technology, data quality and its common infrastructure for people flow on a large scale. Moreover, we provide spatio-temporal data processing service for all researchers through our platform.

Member
Yoshihide Sekimoto (Center for Spatial Information Science, Univ. of Tokyo)
Takehiro Kashiyama (Institute of Industrial Science, Univ. of Tokyo)          Yanbo Pang (Center for Spatial Information Science, Univ. of Tokyo)
Adviser
Ryosuke Shibasaki (Center for Spatial Information Science, Univ. of Tokyo)

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