Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting

TL;DR

This paper introduces MSTFGRN, a novel traffic forecasting model that dynamically captures real-time spatial and temporal dependencies using a data-driven adjacency matrix and a fusion mechanism, outperforming existing methods.

Contribution

The paper presents a new multi-spatio-temporal fusion graph recurrent network with dynamic adjacency matrix generation and a fusion operation for improved traffic prediction accuracy.

Findings

Achieves state-of-the-art performance on four large-scale datasets.

Effectively models real-time spatial dependencies.

Outperforms baseline methods in traffic forecasting accuracy.

Abstract

Traffic forecasting is essential for the traffic construction of smart cities in the new era. However, traffic data's complex spatial and temporal dependencies make traffic forecasting extremely challenging. Most existing traffic forecasting methods rely on the predefined adjacency matrix to model the Spatio-temporal dependencies. Nevertheless, the road traffic state is highly real-time, so the adjacency matrix should change dynamically with time. This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN) to address the issues above. The network proposes a data-driven weighted adjacency matrix generation method to compensate for real-time spatial dependencies not reflected by the predefined adjacency matrix. It also efficiently learns hidden Spatio-temporal dependencies by performing a new two-way Spatio-temporal fusion operation on parallel Spatio-temporal relations at different moments. Finally, global Spatio-temporal dependencies are captured simultaneously by integrating a global attention mechanism into the Spatio-temporal fusion module. Extensive trials on four large-scale, real-world traffic datasets demonstrate that our method achieves state-of-the-art performance compared to alternative baselines.