Estimating people flow from spatiotemporal population data via collective graphical mixture models

Thanks to the prevalence of mobile phones and GPS devices, spatiotemporal population data can be obtained easily. In this article, we propose a mixture of collective graphical models for estimating people flow from spatiotemporal population data. The spatiotemporal population data we use as input is the number of people in each grid cell area over time, which is aggregated information about many individuals; to preserve privacy, they do not contain trajectories of each individual. Therefore, it is impossible to directly estimate people flow. To overcome this problem, the proposed model assumes that transition populations are hidden variables and estimates the hidden transition populations and transition probabilities simultaneously. The proposed model can handle changes of people flow over time by segmenting time-of-day points into multiple clusters, where different clusters have different flow patterns. We develop an efficient variational Bayesian inference procedure for the collective graphical mixture model. In our experiments, the effectiveness of the proposed method is demonstrated by using four real-world spatiotemporal population datasets in Tokyo, Osaka, Nagoya, and Beijing. © 2017 ACM.