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GMAN: A Graph Multi-Attention Network for Traffic Prediction

arXiv 1911.08415 ↗First posted 11 Nov 2019eess.SP cs.LGcited by 433

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Abstract (arXiv)

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

Key facts. Cited by 433 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 52; 2022: 62; 2023: 88; 2024: 94; 2025: 80; 2026: 57.

433 papers.

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  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  4. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  5. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  6. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  7. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  8. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  9. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  10. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  11. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  12. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
38 more
  1. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  2. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  3. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  4. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  5. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  6. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  7. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  8. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  9. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  10. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  11. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  12. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  13. 2023MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecastingcited by 50
  14. 2021AutoCTS: Automated Correlated Time Series Forecasting -- Extended Versioncited by 48
  15. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  16. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  17. 2022Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecastingcited by 42
  18. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  19. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  20. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  21. 2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
  22. 2022Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learningcited by 32
  23. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  24. 2021Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectoriescited by 29
  25. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  26. 2023INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Krigingcited by 27
  27. 2024OpenCity: Open Spatio-Temporal Foundation Models for Traffic Predictioncited by 27
  28. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  29. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  30. 2024AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Predictioncited by 25
  31. 2024Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspectivecited by 24
  32. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  33. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  34. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  35. 2021Spatio-temporal graph neural networks for multi-site PV power forecastingcited by 22
  36. 2021HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecastingcited by 22
  37. 2022Uncertainty Quantification for Traffic Forecasting: A Unified Approachcited by 22
  38. 2023Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bankcited by 22

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