Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency
Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency
Authors
Keywords
Traffic forecasting, Deep learning, Spatial correlation, Graph convolution, Multiple steps ahead forecasting, Sequence to sequence architecture
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