Traffic Flow Forecasting Based on Bi-directional Adaptive Gating Graph Convolutional Networks

Considering the facts that the spatio-temporal dependence of network traffic flow is highly complex and that traffic flow data has noises in practices, this paper proposes a novel spatio-temporal fusion model based on graph neural network for effective traffic flow forecasting. To alleviate negative impacts of data missing, data exception and data noise, a feature fusion block is designed to reconstruct input features and smoothing them within a sliding time window, and then the obtained features are fed into the main body of the proposed model. The main body adopts a design of bi-directional networks to learn respectively the forward and reverse spatio-temporal representation of traffic flow. Both networks share the same structure but with different adjacent matrices. In particular, the causal convolution is used as the temporal feature extractor, and a block of adaptive gated graph convolutional neural network is specially designed for spatial feature extracting, to realize adaptively information aggregation and propagation. Then, a lightweight longitudinal information aggregation layer is constructed to realize information fusion within different local receptive fields. At last, information contributions of the forward and reverse networks are weighed and aggregated with an attention-output module, to establish the expected Bi-directional Adaptive Gating Graph Convolutional Networks (Bi-AGGCN) for traffic flow forecasting. To validate the effectiveness of the proposed model, a series of experiments are carried out on four real traffic flow benchmark datasets, i.e., PEMS03, PEMS04, PEMS07 and PEMS08. Experimental results show that the proposed model Bi-AGGCN can outperform all baseline models over four datasets with three metrics. At the same time, compared with the state-of-the-art baselines, i.e., Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) and Spatial-Temporal Fusion Graph Neural Networks (STFGNN), Bi-AGGCN is dramatically lighter in parameter scale and faster in training time, and achieves higher prediction accuracy at a significant lower cost. © 2023 Science Press. All rights reserved.