  Spatio-temporal prediction plays an important role in many application areas
especially in traffic domain. However, due to complicated spatio-temporal
dependency and high non-linear dynamics in road networks, traffic prediction
task is still challenging. Existing works either exhibit heavy training cost or
fail to accurately capture the spatio-temporal patterns, also ignore the
correlation between distant roads that share the similar patterns. In this
paper, we propose a novel deep learning framework to overcome these issues: 3D
Temporal Graph Convolutional Networks (3D-TGCN). Two novel components of our
model are introduced. (1) Instead of constructing the road graph based on
spatial information, we learn it by comparing the similarity between time
series for each road, thus providing a spatial information free framework. (2)
We propose an original 3D graph convolution model to model the spatio-temporal
data more accurately. Empirical results show that 3D-TGCN could outperform
state-of-the-art baselines.
