mlsanityData to 14 Oct 2026

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

arXiv 2307.00495 ↗First posted 2 Jul 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 44 of its 90 references that are in the map. First posted on arXiv 2 Jul 2023.

Builds on

44 of 90 references.

References · 44

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32 more
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  9. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
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  20. 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 3.

8 papers.

  1. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  2. 2024STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Predictioncited by 1
  3. 2025ADFormer: Aggregation Differential Transformer for Passenger Demand Forecastingcited by 1
  4. 2024Incorporating Long-term Data in Training Short-term Traffic Prediction Modelcited by 0
  5. 2024Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecastingcited by 0
  6. 2026From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Predictioncited by 0
  7. 2026Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depthcited by 0
  8. 2026Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node Statescited by 0