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Found 2 papers, 1 papers with code
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city.
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region.
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