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 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 Obsolete Parts With Alternative Or Secondhand Available 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 Oil Of Oregano 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 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.