Tensor representation-based dynamic graph neural network for traffic flow prediction using auxiliary information

Abstract

Accurately predicting traffic flow is paramount in addressing congestion issues within urban traffic management. However, traditional deep learning methods face limitations in handling the complex dynamic relationships among multi-source data, coupled with large model parameter counts, high computational complexity, and constraints imposed by purely data-driven approaches. To address these challenges, this study introduces the Tensor Representation-based Auxiliary Information Fusion Network (TrafNet). TrafNet integrates various types of traffic data to construct dynamic graph tensors, utilizing dynamic graph convolution to uncover local dynamic correlations across multi-source data. Furthermore, it enhances global dynamic relationship modeling through shared periodic embeddings, enabling the model to more accurately capture temporal dependencies between traffic data. Additionally, TrafNet employs tensor representation learning to decompose dynamic graph tensors into a multiplicative form of multiple small factors, thereby reducing model parameter counts. Lastly, the introduction of Laplacian graph embeddings as initial parameter values for constructing dynamic graph tensor factors enhances model stability and convergence speed. Experimental results demonstrate that TrafNet performs well on three publicly available datasets, achieving higher prediction accuracy and stability compared to traditional methods.