Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting

TL;DR

This paper introduces STAAN, a novel traffic forecasting model that adaptively learns spatial dependencies, incorporates global attention, and captures long-term temporal patterns, outperforming existing methods on real-world datasets.

Contribution

The paper proposes an adaptive dependency matrix and integrates graph attention with dilated convolutions for improved spatial-temporal modeling in traffic forecasting.

Findings

Outperforms state-of-the-art baselines on real-world datasets

Effectively captures long-range spatial dependencies

Improves long-term traffic prediction accuracy

Abstract

Traffic forecasting is one canonical example of spatial-temporal learning task in Intelligent Traffic System. Existing approaches capture spatial dependency with a pre-determined matrix in graph convolution neural operators. However, the explicit graph structure losses some hidden representations of relationships among nodes. Furthermore, traditional graph convolution neural operators cannot aggregate long-range nodes on the graph. To overcome these limits, we propose a novel network, Spatial-Temporal Adaptive graph convolution with Attention Network (STAAN) for traffic forecasting. Firstly, we adopt an adaptive dependency matrix instead of using a pre-defined matrix during GCN processing to infer the inter-dependencies among nodes. Secondly, we integrate PW-attention based on graph attention network which is designed for global dependency, and GCN as spatial block. What's more, a stacked dilated 1D convolution, with efficiency in long-term prediction, is adopted in our temporal block for capturing the different time series. We evaluate our STAAN on two real-world datasets, and experiments validate that our model outperforms state-of-the-art baselines.