Integrating Spatial Self-Attention and Convolution Networks for Improved Car-Sharing Demand Forecasting
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 t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10798450/ |
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| Summary: | 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 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 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. |
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| ISSN: | 2169-3536 |