Shared Bicycle Demand Prediction Based on Hierarchical Spatiotemporal Graph Convolution Network

Shared bicycle demand prediction plays a crucial role in urban public transportation planning, citizen mobility, and environmental protection. However, existing demand prediction models have certain limitations in modeling the coupling relationship between the inflow and outflow of shared bicycle stations. Furthermore, the performance of the model is highly influenced by sparse data, especially for fine-grained shared bicycle demand prediction tasks. To address these challenges, this paper proposes a Hierarchical Spatiotemporal Graph Convolutional Network HST-GCN . Specifically, we apply a hierarchical spatial-temporal feature learning framework to capture both coarse-grained and fine-grained shared bicycle demand features, and utilize a feature transformation matrix to achieve cross-scale fusion of these demand features, alleviating the impact of data sparsity on fine-grained feature modeling. We also design a dynamic coupling graph convolution module to better model the dynamic spatial dependencies between the inflow and outflow of shared bicycle stations. On this basis, we integrate temporal convolution networks and temporal attention mechanisms to capture the spatial-temporal correlations of shared bicycle demand. Extensive experiments are conducted on the Citi Bike dataset from New York and the Divvy dataset from Chicago. The results show that the proposed model outperforms the baseline models in prediction accuracy.