GSTC-Unet: A U-shaped multi-scaled spatiotemporal graph convolutional network with channel self-attention mechanism for traffic flow forecasting

Abstract

Accurate forecasting of traffic flows remains a significant challenge owing to its complex spatiotemporal dependencies. Although existing methods capture some spatiotemporal dependencies and stack them in a channel dimension, long-range sequences, implicit patterns, hidden fine-grained features and multi-scaled spatiotemporal dependencies are often ignored, which makes it difficult to represent the spatiotemporal dependencies comprehensively. To overcome these limitations, a novel deep learning model named Graph Spatiotemporal Channel Unet (U-shaped network) is proposed to achieve accurate and reliable traffic flow forecasting. First, a new temporal encoder-decoder module with causal convolution and transposed convolution is proposed, which can efficiently alleviate the gradient explosion/vanishing in capturing long-range sequences during encoding and decoding. Secondly, a new channel self-attention mechanism is proposed, which can efficiently capture the implicit patterns and hidden fine-grained features between channels and enhance the representation ability of the spatiotemporal dependencies. Thirdly, a new U-shaped multi-scaled spatiotemporal graph convolutional network is proposed to effectively capture the multi-scaled spatiotemporal dependencies. Experiments on two real-world datasets show that the proposed model outperforms baseline models and achieves accurate traffic flow forecasting.