STIGNN: Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

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Abstract
Accurate traffic flow forecasting is a fundamental yet challenging task in intelligent transportation systems, because of the highly nonlinear and complex nature of traffic dynamics. This paper presents a novel Spatio-Temporal Graph Neural Network with Integrated Semantics and Topology (STIGNN). Unlike existing approaches, STIGNN jointly exploits node-level semantic features and graph topological dependencies to enhance spatial representation learning. Specifically, spatial correlations are captured through a ChebNet–GAT hybrid mechanism, which combines multi-hop structural information with semantic attention. Temporal dependencies are modeled using a Gated Temporal Convolutional Network (Gated TCN), enabling efficient learning of long-range sequences. Furthermore, the dual-attention design over node semantics and network topology allows STIGNN to be directly applied to inductive prediction tasks, ensuring strong generalization across diverse network settings. Extensive experiments on two real-world benchmark datasets, METR-LA and PEMS-BAY, demonstrate that STIGNN consistently outperforms state-of-the-art baselines.
Funding Information
  • Innovation fund project for graduate student of China University of Petroleum (26CX04040A)

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