mlsanityData to 7 Oct 2026

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

arXiv 2007.02842 ↗First posted 6 Jul 2020cs.LG stat.MLcited by 525

Not on the map. Why?

Abstract (arXiv)

Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we argue that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable. To this end, we propose two adaptive modules for enhancing Graph Convolutional Network (GCN) with new capabilities: 1) a Node Adaptive Parameter Learning (NAPL) module to capture node-specific patterns; 2) a Data Adaptive Graph Generation (DAGG) module to infer the inter-dependencies among different traffic series automatically. We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks. Our experiments on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections.

Key facts. Cited by 525 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 39; 2022: 64; 2023: 105; 2024: 134; 2025: 104; 2026: 79.

525 papers.

Sort:

By area:

  1. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  3. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  4. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  5. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  6. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  7. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  8. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  9. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  10. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  11. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
  12. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
38 more
  1. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  2. 2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 116
  3. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  4. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  5. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  6. 2024UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Predictioncited by 86
  7. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  8. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  9. 2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
  10. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  11. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  12. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  13. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  14. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  15. 2023MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecastingcited by 50
  16. 2021AutoCTS: Automated Correlated Time Series Forecasting -- Extended Versioncited by 48
  17. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  18. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  19. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  20. 2022Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecastingcited by 42
  21. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  22. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  23. 2024A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Datacited by 39
  24. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  25. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  26. 2021When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?cited by 36
  27. 2022Jointly Contrastive Representation Learning on Road Network and Trajectorycited by 34
  28. 2024How Can Large Language Models Understand Spatial-Temporal Data?cited by 31
  29. 2021Conditional Local Convolution for Spatio-temporal Meteorological Forecastingcited by 30
  30. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  31. 2022Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Modelscited by 28
  32. 2024Segment Anything Model for Road Network Graph Extractioncited by 28
  33. 2023Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecastingcited by 27
  34. 2024OpenCity: Open Spatio-Temporal Foundation Models for Traffic Predictioncited by 27
  35. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  36. 2024Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspectivecited by 24
  37. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  38. 2022Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecastingcited by 23

50 most cited of 525 All 525 (JSON)