Hierarchical multi-scale spatio-temporal semantic graph convolutional network for traffic flow forecasting

Abstract

Accurate traffic flow forecasting is essential for various traffic applications, such as real-time traffic signal control, demand prediction, and route guidance. However, the increasing complexity and non-linearity of big data in the traffic domain pose a challenge for accurate forecasting, necessitating powerful models. This paper proposes a Spatio-temporal model for traffic flow prediction based on Graph Convolutional Neural Network (GCN) and Convolutional Neural Networks (CNN). The hierarchical architecture of Spatio-temporal modeling is utilized to consider multi-scale Spatio-temporal dependencies. We evaluate the proposed model using three real-world datasets, including METR-LA, PeMS04(S), and PeMS04(L). Our experiments demonstrate that the model captures comprehensive spatiotemporal correlations with multi-scale semantics, outperforming features extracted from single domains and non-multi scales. Furthermore, the proposed model is powerful for long-term prediction. We also conduct ablation and architecture studies to highlight the importance of model architecture for Spatiotemporal feature extraction. Our proposed Spatio-temporal model based on GCN and CNN offers a promising approach to traffic flow forecasting in complex traffic scenarios.