Deep spatio-temporal 3D dilated dense neural network for traffic flow prediction

Traffic flow prediction is increasingly vital for the administration of metropolitan areas. Many research on spatio-temporal networks have been explored but the impacts of both spatial and temporal flexibility, complex spatial correlation has not been considered simultaneously. We present the Spatio-Temporal 3D Multiscale Dilated Dense Network (ST-3DMDDN), a novel 3D Convolutional Neural Network (3DCNN) deep learning neural network for the road level and region level traffic flow prediction. It uses autocorrelation analysis' early fusion method for importance sampling, a 3D multiscale dilated convolutional network to capture nearby and remote correlations simultaneously, and a densely connected network for deeper feature extraction. Considering traffic flow's heterogeneity, a new block called the "Spatial and Channel Recalibrate" (SCR) is designed to accurately analyze the correlation contributions. The ST-3DMDDN model is evaluated using three real traffic flows, and the findings indicate that our approach surpasses the performance of the baseline approaches.