  While considering the spatial and temporal features of traffic, capturing the
impacts of various external factors on travel is an essential step towards
achieving accurate traffic forecasting. However, existing studies seldom
consider external factors or neglect the effect of the complex correlations
among external factors on traffic. Intuitively, knowledge graphs can naturally
describe these correlations. Since knowledge graphs and traffic networks are
essentially heterogeneous networks, it is challenging to integrate the
information in both networks. On this background, this study presents a
knowledge representation-driven traffic forecasting method based on
spatial-temporal graph convolutional networks. We first construct a knowledge
graph for traffic forecasting and derive knowledge representations by a
knowledge representation learning method named KR-EAR. Then, we propose the
Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features
as the input of a spatial-temporal graph convolutional backbone network.
Experimental results on the real-world dataset show that our strategy enhances
the forecasting performances of backbones at various prediction horizons. The
ablation and perturbation analysis further verify the effectiveness and
robustness of the proposed method. To the best of our knowledge, this is the
first study that constructs and utilizes a knowledge graph to facilitate
traffic forecasting; it also offers a promising direction to integrate external
information and spatial-temporal information for traffic forecasting. The
source code is available at
https://github.com/lehaifeng/T-GCN/tree/master/KST-GCN.
