  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 graph
attention. To analyze the performance of each element in various environments,
we conducted experiments on four real-world datasets - highway speed, highway
flow, urban speed from a homogeneous road link network, and urban speed from a
heterogeneous road link network. The results demonstrate that the RNN-based
model and the attention-based model show a similar level of performance for
short-term prediction, and the attention-based model outperforms the RNN in
longer-term predictions. The choice of graph convolution and graph attention
makes a larger difference in the RNN-based models. Also, our modified version
of GMAN shows comparable performance with the original with less memory
consumption.
