# AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

arXiv: https://arxiv.org/abs/2011.11004 · HTML: https://mlsanity.com/paper/2011.11004/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 27 of 27)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems](https://mlsanity.com/paper/2209.10622/) (2022)
- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph](https://mlsanity.com/paper/2210.16789/) (2022)
- [Efficient Traffic Prediction Through Spatio-Temporal Distillation](https://mlsanity.com/paper/2501.10459/) (2025)
- [HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction](https://mlsanity.com/paper/2602.05286/) (2026)
- [ChatTraffic: Text-to-Traffic Generation via Diffusion Model](https://mlsanity.com/paper/2311.16203/) (2023)
- [LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2403.16495/) (2024)
- [Counterfactual Explanations for Deep Learning-Based Traffic Forecasting](https://mlsanity.com/paper/2405.00456/) (2024)
- [PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training](https://mlsanity.com/paper/2507.11683/) (2025)
- [AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting](https://mlsanity.com/paper/2209.03356/) (2022)
- [CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph](https://mlsanity.com/paper/2312.08672/) (2023)
- [Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations](https://mlsanity.com/paper/2409.11377/) (2024)
- [SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation](https://mlsanity.com/paper/2306.07292/) (2023)
- [Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting](https://mlsanity.com/paper/2403.19442/) (2024)
- [Context-aware knowledge graph framework for traffic speed forecasting using graph neural network](https://mlsanity.com/paper/2407.17703/) (2024)
- [ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations](https://mlsanity.com/paper/2411.01267/) (2024)
- [FlowDistill: Scalable Traffic Flow Prediction via Distillation from LLMs](https://mlsanity.com/paper/2504.02094/) (2025)
- [A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2506.02609/) (2025)
- [A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalism](https://mlsanity.com/paper/2507.00085/) (2025)
- [Multi-Level Service Performance Forecasting via Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2508.07122/) (2025)
- [MGTS-Net: Exploring Graph-Enhanced Multimodal Fusion for Augmented Time Series Forecasting](https://mlsanity.com/paper/2510.16350/) (2025)
- [A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting](https://mlsanity.com/paper/2512.17453/) (2025)
- [Efficient Prompt Learning for Traffic Forecasting](https://mlsanity.com/paper/2605.08273/) (2026)
- [INDEQS: Informed Neural controlled Differential EQuationS](https://mlsanity.com/paper/2606.19138/) (2026)
- [Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Prediction](https://mlsanity.com/paper/2609.31667/) (2026)

## Abstract (arXiv)

Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic forecasting tasks when compared to traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines.