Dynamic Demand Forecasting for Bike-Sharing E-Fences Using a Hybrid Deep Learning Framework with Spatio-Temporal Attention

The rapid expansion of bike-sharing systems has introduced significant management challenges related to spatial-temporal demand fluctuations and inefficient e-fence capacity allocation. This study proposes a Spatio-Temporal Graph Attention Transformer Network (STGATN), a novel hybrid deep learning framework for dynamic demand forecasting in bike-sharing e-fence systems. The model integrates Graph Convolutional Networks to capture complex spatial dependencies among urban functional zones, Bi-LSTM networks to model temporal patterns with periodic variations, and attention mechanisms to dynamically incorporate weather impacts. By constructing a city-level graph based on POI-derived e-fences and implementing multi-source feature fusion through Transformer architecture, the STGATN effectively addresses the limitations of static capacity allocation strategies. The experimental results from Shenzhen’s Nanshan District demonstrate the performance, with the STGATN model achieving an overall Mean Absolute Error (MAE) of 0.0992 and a Coefficient of Determination (R2) of 0.8426. This significantly outperforms baseline models such as LSTM (R2: 0.6215) and a GCN (R2: 0.5488). Ablation studies confirm the model’s key components are critical; removing the GCN module decreased R2 by 12 percentage points to 0.7411, while removing the weather attention mechanism reduced R2 by nearly 5 percentage points to 0.8034. The framework provides a scientific basis for dynamic e-fence capacity management, advancing spatio-temporal prediction methodologies for sustainable transportation.