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Found 7 papers, 2 papers with code
Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.
Ridesourcing Car Detection by Transfer Learning
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban Computing
Therefore, a comprehensive and multifaceted dataset is required to enable more extensive studies in urban computing.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
