Citation

Fan J, Weng W, Tian H, Wu H, Zhu F, Wu J. Neural. Netw. 2024; 172: e106093.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.neunet.2023.106093

PMID

38228022

Abstract

Traffic Prediction based on graph structures is a challenging task given that road networks are typically complex structures and the data to be analyzed contains variable temporal features. Further, the quality of the spatial feature extraction is highly dependent on the weight settings of the graph structures. In the transportation field, the weights of these graph structures are currently calculated based on factors like the distance between roads. However, these methods do not take into account the characteristics of the road itself or the correlations between different traffic flows. Existing approaches usually pay more attention to local spatial dependencies extraction while global spatial dependencies are ignored. Another major problem is how to extract sufficient information at limited depth of graph structures. To address these challenges, we propose a Random Graph Diffusion Attention Network (RGDAN) for traffic prediction. RGDAN comprises a graph diffusion attention module and a temporal attention module. The graph diffusion attention module can adjust its weights by learning from data like a CNN to capture more realistic spatial dependencies. The temporal attention module captures the temporal correlations. Experiments on three large-scale public datasets demonstrate that RGDAN produces predictions with 2%-5% more precision than state-of-the-art methods.

Language: en

Keywords

Deep learning; Attention networks; Graph convolutional network; Spatial–temporal embedding; Spatial–temporal model; Traffic prediction