# Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

arXiv: https://arxiv.org/abs/2006.12715 · HTML: https://mlsanity.com/paper/2006.12715/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 23 of 23)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Interpreting and Unifying Graph Neural Networks with An Optimization Framework](https://mlsanity.com/paper/2101.11859/) (2021)
- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities](https://mlsanity.com/paper/2202.03630/) (2022)
- [Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging](https://mlsanity.com/paper/2109.12144/) (2021)
- [GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2311.04245/) (2023)
- [A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management](https://mlsanity.com/paper/2306.02806/) (2023)
- [Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task](https://mlsanity.com/paper/2110.04228/) (2021)
- [Positive-Unlabeled Node Classification with Structure-aware Graph Learning](https://mlsanity.com/paper/2310.13538/) (2023)
- [Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting](https://mlsanity.com/paper/2409.15662/) (2024)
- [ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data](https://mlsanity.com/paper/2412.10912/) (2024)
- [Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems](https://mlsanity.com/paper/2108.05385/) (2021)
- [GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation](https://mlsanity.com/paper/2207.00838/) (2022)
- [Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution](https://mlsanity.com/paper/2303.16741/) (2023)
- [RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting](https://mlsanity.com/paper/2512.21685/) (2025)
- [Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network](https://mlsanity.com/paper/2607.26733/) (2026)
- [Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network](https://mlsanity.com/paper/2401.02723/) (2024)
- [TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting](https://mlsanity.com/paper/2407.18519/) (2024)
- [From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction](https://mlsanity.com/paper/2604.11198/) (2026)
- [Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth](https://mlsanity.com/paper/2606.09539/) (2026)
- [Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2607.26467/) (2026)
- [DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments](https://mlsanity.com/paper/2608.00402/) (2026)

## Abstract (arXiv)

Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion.