Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis

Abstract

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers, and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviors and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.