2019

3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting

Yu, Bing, Li, Mengzhang, Zhang, Jiyong et al.

Understand

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).

Built on

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