Integrating Spatial Self-Attention and Convolution Networks for Improved Car-Sharing Demand Forecasting

Integrating Spatial Self-Attention and Convolution Networks for Improved Car-Sharing Demand Forecasting

Blog Article

The core challenge facing the field of car-sharing demand forecasting lies in the innovative construction of models that effectively capture the intricate spatio-temporal merlot redbud tree for sale variations in the data.Current methods face two particularly significant challenges: first, Current models struggle to capture the mutual influences and connections between nearby parking stations; second, when addressing data involving long time series, traditional methods often encounter the dilemma of gradient vanishing or exploding.In view of this, we proposed the SG-SCINet prediction model, which cleverly combines the advantages of the spatial self-attention mechanism and the sample convolution and interaction network (SCINet).By introducing the self-attention module, SG-SCINet effectively analyzes spatial and functional interactions, improving the prediction of car-sharing demand.

This series of designs significantly improves the moondrop quarks model’s adaptability in complex spatio-temporal environments.Experimental verification shows that the SG-SCINet model shows significant advantages over a single model in terms of prediction accuracy.