Do We Really Need Graph Neural Networks for Traffic Forecasting?
This work addresses efficiency issues in traffic forecasting for practitioners, offering a competitive alternative to GNNs, though it is incremental in nature.
The paper tackles the inefficiency of graph neural networks (GNNs) in traffic forecasting by proposing SimST, a simpler spatio-temporal approach that improves prediction throughput by up to 39 times while achieving comparable performance.
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to establish spatial dependencies between nodes, and thus inevitably inherit GNNs' notorious inefficiency. Given these facts, in this paper, we propose an embarrassingly simple yet remarkably effective spatio-temporal learning approach, entitled SimST. Specifically, SimST approximates the efficacies of GNNs by two spatial learning techniques, which respectively model local and global spatial correlations. Moreover, SimST can be used alongside various temporal models and involves a tailored training strategy. We conduct experiments on five traffic benchmarks to assess the capability of SimST in terms of efficiency and effectiveness. Empirical results show that SimST improves the prediction throughput by up to 39 times compared to more sophisticated STGNNs while attaining comparable performance, which indicates that GNNs are not the only option for spatial modeling in traffic forecasting.