Web: http://arxiv.org/abs/2111.07513

Jan. 14, 2022, 2:11 a.m. | Yuyol Shin, Yoonjin Yoon

cs.LG updates on arXiv.org arxiv.org

Traffic forecasting plays a crucial role in intelligent transportation
systems. The spatial-temporal complexities in transportation networks make the
problem especially challenging. The recently suggested deep learning models
share basic elements such as graph convolution, graph attention, recurrent
units, and/or attention mechanism. In this study, we designed an in-depth
comparative study for four deep neural network models utilizing different basic
elements. For base models, one RNN-based model and one attention-based model
were chosen from previous literature. Then, the spatial feature extraction
layers in the models were substituted with graph convolution and …

arxiv deep deep learning for forecasting learning on study

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