TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting

Abstract

Accurate traffic forecasting is a fundamental challenge of location-based systems. Recent works were able to achieve state-of-the-art results by incorporating Graph Convolutional Networks (GCN) to capture spatial dependencies in the data. However, these works rely on a fixed latent feature representation of the underlying graph structure, failing to exploit the rich spatial information offered by the road network. In this paper, we propose the Topologically Enhanced Spatial-Temporal Graph Convolutional Network (TEST-GCN), a novel graph convolution model for road traffic speed forecasting based on floating car data, aiming to better capture the spatial dependencies in the data by fully exploiting the characteristics of the road network. We introduce the node and edge embedding layers, using topological attributes to iteratively improve the latent feature representation of the road network. We show that our model effectively captures both spatial and temporal dependencies in the data, consistently outperforming state-of-the-art methods in road traffic speed prediction, achieving approximately 50 % reduction in model size and 33% improvement in empirical computational times.