STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural
  Networks for Traffic Prediction
Xunlian Luo Chunjiang Zhu Detian Zhang Qing Li
Traffic prediction has been an active research topic in the domain of
spatial-temporal data mining. Accurate real-time traffic prediction is
essential to improve the safety, stability, and versatility of smart city
systems, i.e., traffic control and optimal routing. The complex and highly
dynamic spatial-temporal dependencies make effective predictions still face
many challenges. Recent studies have shown that spatial-temporal graph neural
networks exhibit great potential applied to traffic prediction, which combines
sequential models with graph convolutional networks to jointly model temporal
and spatial correlations. However, a survey study of graph learning,
spatial-temporal graph models for traffic, as well as a fair comparison of
baseline models are pending and unavoidable issues. In this paper, we first
provide a systematic review of graph learning strategies and commonly used
graph convolution algorithms. Then we conduct a comprehensive analysis of the
strengths and weaknesses of recently proposed spatial-temporal graph network
models. Furthermore, we build a study called STG4Traffic using the deep
learning framework PyTorch to establish a standardized and scalable benchmark
on two types of traffic datasets. We can evaluate their performance by
personalizing the model settings with uniform metrics. Finally, we point out
some problems in the current study and discuss future directions. Source codes
are available at https://github.com/trainingl/STG4Traffic.