DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction

The past 10 years have witnessed the rapid growth of global mobile cellular traffic demands due to the popularity of mobile devices. While accurate traffic prediction becomes extremely important for stable and high-quality Internet service, the performance of existing methods is still poor due to three challenges: complicated temporal variations including burstiness and long periods, multi-variant impact factors such as the point of interest and day of the week, and potential spatial dependencies introduced by the movement of population. While existing traditional methods fail in characterizing these features, especially the latter two, deep learning models with powerful representation ability give us a chance to consider these from a new perspective. In this article, we propose Deep Traffic Predictor (DeepTP), a deep-learning-based end-toend model, which forecasts traffic demands from spatial-dependent and long-period cellular traffic. DeepTP consists of two components: a general feature extractor for modeling spatial dependencies and encoding the external information, and a sequential module for modeling complicated temporal variations. In the general feature extractor, we introduce a correlation selection mechanism for a spatial modeling and embedding mechanism to encode external information. Moreover, we apply a seq2seq model with attention mechanism to build the sequential model. Extensive experiments based on large-scale mobile cellular traffic data demonstrate that our model outperforms the state-of-the-art traffic prediction models by more than 12.31 percent.