Traffic Speed Forecasting Via Spatio-Temporal Attentive Graph Isomorphism Network

Abstract

Traffic forecasting is of particular interest in intelligent transportation systems (ITS). This problem is challenging owing to the complicated spatio-temporal dependencies between different areas in a road sensor network. Previous approaches have applied various deep learning methods for traffic forecasting, e.g., leveraging graph convolutional networks (GCNs) for spatial correlation modeling and utilizing recurrent neural networks (RNNs) to capture temporal traffic evolutions. However, the existing GCN-based models can not adequately distinguish the non-Euclidean topological structure of road traffic and are easily affected by random traffic noise. This work proposes an end-to-end framework to capture spatial dependencies through graph isomorphism network, while explicitly taking network topologic similarities into account and leveraging symmetric traffic for learning the traffic conditions. Extensive experiments on two real-world traffic datasets demonstrate the superiority of our proposed approach.