mlsanityData to 7 Oct 2026

Graph Neural Network for Traffic Forecasting: A Survey

arXiv 2101.11174 ↗First posted 27 Jan 2021cs.LG cs.AIcited by 260

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Abstract (arXiv)

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.

Key facts. Cited by 260 papers in the map (data through 7 Oct 2026). Builds on 93 of its 263 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

93 of 263 references.

References · 93

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38 more
  1. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
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  3. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
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50 most cited of 93 All 93 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 41; 2023: 57; 2024: 71; 2025: 49; 2026: 25.

260 papers.

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  1. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  2. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  3. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  4. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  5. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  6. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  7. 2022Fairness in Graph Mining: A Surveycited by 76
  8. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  9. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  10. 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modelingcited by 63
  11. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  12. 2025Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecastingcited by 53
38 more
  1. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  2. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  3. 2023Knowledge Distillation on Graphs: A Surveycited by 38
  4. 2021Category Theory in Machine Learningcited by 36
  5. 2022Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networkscited by 33
  6. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  7. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  8. 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
  9. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  10. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  11. 2022Graph Neural Networks in IoT: A Surveycited by 27
  12. 2025TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecastingcited by 27
  13. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 26
  14. 2023From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyondcited by 26
  15. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  16. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  17. 2022DeepCut: Unsupervised Segmentation using Graph Neural Networks Clusteringcited by 25
  18. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
  19. 2021Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practicescited by 24
  20. 2022Uncertainty Quantification for Traffic Forecasting: A Unified Approachcited by 22
  21. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
  22. 2023Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networkscited by 22
  23. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  24. 2023Graph Neural Network-based EEG Classification: A Surveycited by 21
  25. 2021Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecastingcited by 19
  26. 2023Deep learning for dynamic graphs: models and benchmarkscited by 19
  27. 2023Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networkcited by 19
  28. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 18
  29. 2022Towards Sparsification of Graph Neural Networkscited by 18
  30. 2023Curvature Filtrations for Graph Generative Model Evaluationcited by 18
  31. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  32. 2024BACKTIME: Backdoor Attacks on Multivariate Time Series Forecastingcited by 18
  33. 2023Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunitiescited by 16
  34. 2023The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillationcited by 16
  35. 2024NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noisecited by 16
  36. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  37. 2021AutoGL: A Library for Automated Graph Learningcited by 14
  38. 2023Dynamic Interactive Relation Capturing via Scene Graph Learning for Robotic Surgical Report Generationcited by 14

50 most cited of 260 All 260 (JSON)