FD-TGCN: fast and dynamic temporal graph convolution network for traffic flow prediction

Lijun Sun, Mingzhi Liu, Guanfeng Liu, Xiao Chen, Xu Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The traffic flow prediction has recently been challenged due to its complicated dynamic spatial–temporal features. In terms of temporal modeling, the dilated convolution used to model the temporal relationship consumes more training time. In terms of spatial modeling, traffic flow prediction results are affected not only by the dynamic connection spatial relationship, but also by the changes of traffic road structure, which is ignored by most methods. In order to address these concerns, we propose a new traffic flow prediction method which is called Fast and Dynamic Temporal Graph Convolution Network (FD-TGCN). FD-TGCN comprises a temporal module and a spatial module. In the temporal module, we propose a Fast Time Convolution Network (FTCN) to reduce the training time. The spatial module improves prediction accuracy by separately modeling dynamic connection spatial relationship and the change in the structure of the road. A series of experiments have shown that compared with the baseline models, our proposed method achieves an average accuracy improvement of 1.3% and 1.85% on two datasets, respectively, while saving an average training time of 293.55%.

Original languageEnglish
Article number102291
Pages (from-to)1-10
Number of pages10
JournalInformation Fusion
Volume106
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Traffic flow forecasting
  • Spatial–temporal relationships
  • Graph convolutional network

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