TADGCN: A Time-Aware Dynamic Graph Convolution Network for long-term traffic flow prediction

Abstract

Traffic flow prediction is a crucial component of the Intelligent Transportation System (ITS) with substantial implications for traffic management and daily travel. While short-term traffic forecasting has shown promise, long-term prediction presents unique challenges. The existing short-term approaches fall short in long-term scenarios due to two primary issues: (1) ignoring modeling the interaction relationships among dynamic spatial dependencies, and (2) extracting long-term temporal dependencies without capturing the time information in-depth. To mitigate the above issues, we propose a novel model called TADGCN, a Time-Aware Dynamic Graph Convolution Network, to effectively perform long-term traffic flow prediction. TADGCN captures latent interactions among dynamic spatial dependencies using Attention-Driven Dynamic Adaptive Graph Convolution Network (ADAGCN) modules. The time-aware capability is effectively enhanced using Time-Aware Joint Multi-View Temporal Encoder (TJMTE) modules due to the construction of a time-aware matrix. We conduct diversified experiments on three real-world datasets (England, PEMS04, and PEMS08) to evaluate the performance of our proposed method TADGCN. The results reveal that TADGCN achieves the best accuracy in all cases against 16 competitive state-of-the-art baselines.