Forecasting Urban dynamics with Mobility Logs by Bilinear Poisson Regression
Masamichi Shimosaka (Tokyo Institute of Technology), Keisuke Maeda (The University of Tokyo), Takeshi Tsukiji (The University of Tokyo), Kota Tsubouchi
UbiComp '15 (the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing), 2015/9 pp.535-546
Machine Learning Data Science
- 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.
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