Predicting behaviors of a population from location-oriented log data from smartphones, i.e., urban population dynamics, has become more common in mobile and pervasive computing. A bilinear representation approach has been proposed to improve the prediction accuracy of urban population dynamics by adding contexts such as geographical information and day of the week. However, this approach has a strong limitation in that additional contexts can not be directly utilized in this representation with a unified manner. To resolve this issue, we propose a new predictive model for urban population dynamics based on multilinear Poisson regression so as to handle multiple contexts in a systematic manner. The model is parameterized using a tensor and can be optimized by using an efficient convex optimization with a sequence of matrix parameter optimizations. An empirical evaluation with large-scale smartphone location data showed that our model outperforms conventional approaches.

Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contributing to a more prosperous society. To deal with the non-linear effects on urban dynamics from external factors, such as day of the week, national holiday, or weather, we propose a low-rank bilinear Poisson regression model, for a novel and flexible representation of urban dynamics predictive analysis. The results obtained from an experiment with one year's worth of mobility records suggest the high prediction accuracy of the proposed model. We also introduce the following applications: regional event detection via irregularities, visualization of urban dynamics corresponding to urban demographics, and extraction of urban demographics of unknown point of interests.