The Hierarchical Clustering of Human Mobility Behaviors

Human mobility flow prediction forecasts the number of passengers coming into (inflows) or leaving from (outflows) every region of a city. It is crucial for many applications such as mobile marketing or route optimization. People have proposed deep learning methods to predict human mobility flows. However, existing methods neglect the hierarchical nature of human mobility behaviors. Each of us is unique, and we live beyond our neighborhoods. In the process of cross-regional activities, humans migrate in the hierarchical structure of buildings, neighborhoods, regions, cities, and countries. In this article, we propose a predictive framework, hierarchical fuzzy C-means (Hierarchical-FCM)-residual networks (ResNets), to capture the hierarchical structure of human mobility for prediction. First, we offer a Hierarchical-FCM clustering algorithm that is trained to learn the relationship between proximity and road network hierarchically on large human mobility data. Second, we design a fusion model to incorporate the knowledge learned from hierarchical clusters into deep ResNets to improve prediction accuracy. We compare our framework with 25 existing state-of-the-art models, ranging from traditional time-series and machine learning predictors, such as ARIMA and RNN, to the latest deep learning methods designed for human mobility prediction, such as ST-ResNets and DeepST. Our framework outperforms all existing models, by a margin of 2%–68% in terms of prediction accuracy, showing its effectiveness. We are among the first to incorporate the hierarchical structure of human mobility into location clustering for human mobility flow prediction. We empirically show that incorporating such knowledge significantly improves prediction performance.