计算机科学
图形
解码方法
卷积(计算机科学)
编码(内存)
频道(广播)
编码器
数据挖掘
代表(政治)
人工智能
模式识别(心理学)
算法
理论计算机科学
人工神经网络
计算机网络
政治
政治学
法学
操作系统
作者
Wangzhi Yu,Xiaoling Huang,Yilei Qiu,Shuai Zhang,Q. Chen
标识
DOI:10.1016/j.eswa.2023.120724
摘要
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.
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