SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction

Large-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. Meanwhile, the massive spatial units significantly increase the computational cost, limiting model efficiency. To address these limitations, we propose a novel model for large-scale crowd flow prediction, namely the Stratified Compressive Sensing Network (SCS-Net). First, we develop a spatially stratified module that posterior adaptively extracts the underlying spatially stratified structure, effectively modeling spatial heterogeneity. Then, we develop compressive sensing modules to compress redundant information from massive spatial units and learn shared crowd flow patterns, enabling efficient prediction. Finally, we conduct experiments on a large-scale real-world dataset. The results demonstrate that SCS-Net outperforms deep learning baseline models by 35.25–139.2% in MAE and 26.3–112.4% in RMSE while reducing GFLOPs by 53–1067 times and shortening training time by 3.1–83.2 times compared to prevalent spatio-temporal prediction models. Moreover, the spatially stratified structure extracted by SCS-Net offers valuable interpretability for spatial heterogeneity in crowd flow patterns, providing deeper insights into urban functional layouts.