Data to 14 Oct 2026

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

arXiv 1709.04875 ↗First posted 14 Sep 2017cs.LG stat.MLcited by 1,136

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Abstract (arXiv)

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

Key facts. Cited by 1,136 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Sep 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 147; 2022: 161; 2023: 200; 2024: 238; 2025: 230; 2026: 160.

1,136 papers.

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2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 172
2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 151
2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145

38 more

2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 143
2021Graph Learning: A Surveycited by 141
2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 132
2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 118
2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 117
2022Out-Of-Distribution Generalization on Graphs: A Surveycited by 97
2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 90
2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 83
2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
2022Spatio-Temporal Self-Supervised Learning for Traffic Flow Predictioncited by 77
2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
2022A Survey on Graph Representation Learning Methodscited by 66
2023AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimationcited by 64
2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 63
2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
2021A Survey on Embedding Dynamic Graphscited by 60
2022Normalizing Flows for Human Pose Anomaly Detectioncited by 60
2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52

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