A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting. (arXiv:2111.07513v2 [cs.LG] UPDATED)

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 …

arxiv deep learning forecasting learning study