TY - JOUR
T1 - FD-TGCN
T2 - fast and dynamic temporal graph convolution network for traffic flow prediction
AU - Sun, Lijun
AU - Liu, Mingzhi
AU - Liu, Guanfeng
AU - Chen, Xiao
AU - Yu, Xu
PY - 2024/6
Y1 - 2024/6
N2 - 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%.
AB - 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%.
KW - Traffic flow forecasting
KW - Spatial–temporal relationships
KW - Graph convolutional network
UR - http://www.scopus.com/inward/record.url?scp=85185249758&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2024.102291
DO - 10.1016/j.inffus.2024.102291
M3 - Article
AN - SCOPUS:85185249758
SN - 1566-2535
VL - 106
SP - 1
EP - 10
JO - Information Fusion
JF - Information Fusion
M1 - 102291
ER -