Do We Really Need Graph Neural Networks for Traffic Forecasting?
- URL: http://arxiv.org/abs/2301.12603v1
- Date: Mon, 30 Jan 2023 01:30:04 GMT
- Title: Do We Really Need Graph Neural Networks for Traffic Forecasting?
- Authors: Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi,
Roger Zimmermann
- Abstract summary: We propose an embarrassingly simple yet remarkably effective approach, entitled SimST.
SimST approximates efficacies of GNNs by two spatial learning techniques.
We conduct experiments on five traffic benchmarks to assess the capability of SimST in terms of efficiency and effectiveness.
- Score: 44.25906613303993
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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