# HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting

arXiv: https://arxiv.org/abs/2209.01967 · HTML: https://mlsanity.com/paper/2209.01967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)
- [Orthogonal Convolutional Neural Networks](https://mlsanity.com/paper/1911.12207/) (2019)
- [Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting](https://mlsanity.com/paper/2105.04100/) (2021)
- [Trellis Networks for Sequence Modeling](https://mlsanity.com/paper/1810.06682/) (2018)
- [Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting](https://mlsanity.com/paper/2109.05225/) (2021)

## Abstract (arXiv)

The construction of spatiotemporal networks using graph convolution networks (GCNs) has become one of the most popular methods for predicting traffic signals. However, when using a GCN for traffic speed prediction, the conventional approach generally assumes the relationship between the sensors as a homogeneous graph and learns an adjacency matrix using the data accumulated by the sensors. However, the spatial correlation between sensors is not specified as one but defined differently from various viewpoints. To this end, we aim to study the heterogeneous characteristics inherent in traffic signal data to learn the hidden relationships between sensors in various ways. Specifically, we designed a method to construct a heterogeneous graph for each module by dividing the spatial relationship between sensors into static and dynamic modules. We propose a network decentralization attention based heterogeneity-aware graph convolution network (HAGCN) method that aggregates the hidden states of adjacent nodes by considering the importance of each channel in a heterogeneous graph. Experimental results on real traffic datasets verified the effectiveness of the proposed method, achieving a 6.35% improvement over the existing model and realizing state-of-the-art prediction performance.