STEGNN: Spatial-Temporal Embedding Graph Neural Networks for Road Network Forecasting

Abstract

As intelligent transportation systems (ITS) are now being integrated into our everyday lives, it has been widely accepted that forecasting road networks is a promising killer engine for ITS with high social and economic benefits. However, current solutions ignore the heterogeneity of spatial-temporal traffic data and fail to capture hidden spatial-temporal correlations. This paper presents STEGNN: a novel spatial-temporal embedding graph neural network for road network forecasting. The key idea of STEGNN is utilizing Cosine Similarity to generate a high-quality temporal graph and thus fills the gap between the temporal-spatial correlations for traffic graph, which includes (i) a novel approach to construct temporal graph based on temporal-spatial similarity from traffic graphs, which is much more accurate on measured similarity of time series claimed by previous methods; (ii) an advanced spatial-temporal embedding model to exploit spatial-temporal dependencies by leveraging specific arrangements of temporal and spatial graphs; and (iii) an effective framework that gasps extensive spatial-temporal dependencies in the long-term by mixing multi-layer graph convolution with dilated convolution to understand wide-range spatial-temporal features. Extensive evaluations validate STEGNN by applying it to real-world traffic graphs and indicate that STEGNN outperforms state-of-the-art solutions with much more accurate forecasting of road networks.