CRBF-Net: Convolutional RBF Network for Key Grid Cells Selection in Spatial-Temporal Flow Prediction

The selection of key grid cells is critical for enhancing the interpretability and reliability of urban spatial-temporal flow prediction. Manual selection by domain experts can be inconsistent and time-consuming, while recent deep learning approaches that rely on feature visualization often produce unstable results due to sensitivity to input changes. In this work, we propose CRBF-Net, a convolutional radial basis function network that decouples feature representations from prediction outputs by introducing spatially localized basis kernels to prioritize selection stability. Our proposed method is evaluated on real-world telecom mobility data from Taipei, Taiwan. Experimental results demonstrate that CRBF-Net ensures high selection consistency and enhances the transferability of spatial insights across various downstream prediction models, including LSTM, ANN, and KNN. MAPE reductions were observed across all locations, with improvements of up to 8.24% compared to prior visualization-based selection strategies. Additionally, stability analysis via the Jaccard Index confirms that CRBF-Net maintains significantly higher spatial consistency across multiple independent trials. These results highlight the robustness and practical utility of CRBF-Net in selecting meaningful spatial regions, providing a stable and generalizable approach to key grid cells selection in urban flow prediction while contributing to the development of trustworthy and interpretable GeoAI systems.