Inferring Pedestrian Flow while protecting privacy

With recent advances in wireless and mobile networks, location information of pedestrians can be recorded in a variety of spaces such as exhibition halls and shopping malls. However, location information of pedestrians is often aggregated for protecting privacy. Aggregated data is a set of incoming and outgoing pedestrian counts at each location. So, it is not straightforward to know pedestrian flow between locations from the aggregated data. In this exhibition, we propose a probabilistic model for inferring latent pedestrian flow between locations using only aggregated data. By incorporating distributions of travel duration between locations, the proposed model can precisely estimate the pedestrian flow between locations. Our model enables us to understand pedestrian mobility patterns while protecting privacy, which provides better navigation and location-based mobile advertising.

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