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This paper introduces MSPF-Net, a novel framework for cellular traffic forecasting that integrates multimodal data, including temporal, spatial, and external urban news signals. By employing a Spatiotemporal-Frequency Traffic Encoder and a Peak Enhancement Module, the model captures both intrinsic traffic patterns and burst behavior, leading to more accurate predictions. Experiments across multiple datasets reveal that this approach significantly enhances forecasting performance compared to traditional methods that overlook the influence of external factors and burst dynamics.
Jointly modeling traffic dynamics and urban events can drastically improve cellular traffic forecasting accuracy.
Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Real-world traffic often exhibits bursty endogenous dynamics and disturbances triggered by external urban events, which makes reliable prediction highly challenging. Most existing spatiotemporal traffic forecasting methods primarily focus on intrinsic traffic patterns or structural relationships within a single modality, and rarely model burst behavior together with exogenous contextual signals. To address this issue, we propose \textbf{MSPF-Net}, a multimodal cellular traffic forecasting framework that integrates external contextual information. Specifically, MSPF-Net consists of a Spatiotemporal-Frequency Traffic Encoder for capturing temporal, spatial, and spectral traffic patterns, a Peak Enhancement Module for extracting burst-aware representations of sudden spikes, a News Context Representation Module for encoding urban news streams into exogenous contextual embeddings, and a Dynamic Fusion Prediction Module for adaptively integrating these heterogeneous signals to generate forecasts. Experiments on the Milano, Trento, and LTE traffic datasets demonstrate that jointly modeling traffic dynamics, burst patterns, and news contextual signals can effectively improve forecasting performance.
