MOIRE: Mixed-Order Poisson Regression towards Fine-grained Urban Anomaly Detection at Nationwide Scale

摘要

The analysis of crowd flow in urban regions (urban dynamics) from GPS traces has been actively explored over the last decade. However, the existing prediction models assume that the population density in the analysis area is almost uniform, making it difficult to analyze fine-grained urban dynamics on a nationwide scale, where urban and rural areas coexist. In this paper, we propose a predictive model, called mixed-order Poisson regression (MOIRE), to capture changes in active populations nationwide by combining lower-order patterns and higher-order interaction effects. The proposed method utilizes multiple pieces of contextual information that greatly affect crowd flows (e.g., time-of-day, day-of-the-week, weather situation, holiday calendar information). We evaluated MOIRE on two massive GPS datasets gathered in urban regions at different scales. The results show that it has better predictive performance than the state-of-the- art method. Moreover, we implemented an anomaly detection system in urban dynamics for the whole nation of Japan in accordance with MOIRE specifications. This application enabled us to confirm MOIRE’s performance intuitively.