ASISTGCRN: A novel approach to traffic prediction using attention-based spatiotemporal graph networks

Abstract

Traffic flow forecasting is a fundamental task in intelligent transportation systems, directly supporting traffic control, congestion mitigation, and urban mobility planning. However, prediction remains difficult owing to nonlinear dynamics, rapidly changing spatiotemporal dependencies, and the integration of heterogeneous data. This paper proposes ASISTGCRN, an attention-based spatiotemporal graph convolutional recurrent network that introduces a tri-cycle segmentation strategy to capture recent, daily, and weekly periodic patterns. Central to the framework is a spatiotemporal block that integrates multi-head temporal and spatial attention with Dynamic Time Warping to account for both dynamic local dependencies and remote functional similarities. Adaptive graph convolution and gated recurrent units are employed to jointly model spatial structures and temporal sequences, while Transformer- and Informer-based attention layers are further applied to capture long-range dependencies, yielding two model variants, T-ASISTGCRN and I-ASISTGCRN. Extensive experiments on four widely used benchmark datasets (PEMS03, PEMS04, PEMS07M, PEMS08) demonstrate that ASISTGCRN consistently outperforms 17 baseline approaches across MAE, RMSE, and MAPE metrics. Ablation studies further verify the contribution of the tri-cycle segmentation, spatiotemporal block, and attention modules. These results indicate that the proposed framework offers improved robustness and accuracy in traffic flow prediction and has practical relevance for the design of advanced traffic management and planning strategies in complex urban environments.