Abstract

Forecasting the spatiotemporal distribution of mobile traffic is crucial for efficient cellular network management. Despite the superior performance of many deep learning studies, they remain inadequate for multi-city forecasting due to the neglect of geospatial effects in deep models. Specifically, spatial heterogeneity and geographical similarity suggest that distinct patterns exist within different urban regions, while shared patterns exist across different cities. To address this gap, this study proposes a Collaborative Specific-Shared Knowledge Learning (CSSKL) framework based on a meta-learning strategy for mobile traffic forecasting in two distinct cities. CSSKL consists of two key components: (1) a geographical learning module for capturing specific patterns using regional customization and (2) a geographical transfer strategy for capturing shared patterns using an attention mechanism. The effectiveness of CSSKL is validated through real-world mobile traffic datasets from two cities, namely Milan and Trentino, Italy. Experimental results demonstrate that CSSKL outperforms all baseline models, yielding a significant improvement in cross-city forecasting performance.