# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 5 Conclusions  
Traffic-flow prediction is an essential part of ITS research on improving traffic efficiency and safety. Both traffic managers and travelers can benefit from timely and accurate traffic-flow prediction. However, current spatial-temporal traffic-flow-prediction models rarely consider the periodicity and heterogeneity of traffic-flow changes from the network perspective.  
To address this issue, in this paper, we proposed the spatial-temporal self-attention graph convolution network (STA-GCN) model, considering both periodic characteristics and the dynamic spatial-temporal dependence of network traffic flow. Specifically, Fourier decomposition was used to decompose the time series into periodic variables and one-hot encoding was used to distinguish the heterogeneity. Therefore, the proposed model incorporated spatial-temporal self-attention mechanisms into graph convolutional networks and time-gated convolutional networks to capture dynamic changes in traffic-flow characteristics.  
Through a performance comparison with previous prediction models, the proposed model demonstrated a better performance on selected traffic-flow datasets, which indicates the effectiveness of STA-GCN in traffic-flow-prediction tasks. Furthermore, the visual analysis of the spatial-attention-prediction process was conducted to illustrate the interpretability of our model. Moreover, the proposed model is applicable to the prediction of other traffic parameters, including traffic speed and time occupancy, as well as passenger-flow predictions for subways and bus networks.  
Although the STA-GCN model captures temporal-spatial traffic-flow patterns through the self-attention mechanism, the structure of the traffic-node network is fixed and unchanging. In future research, a promising method to improve the graph convolutional  
Figure 9: Visualization results of the spatial attention mechanism.  
network would be to incorporate graph-theory-related approaches to enable the network to acquire dynamic characteristics. Furthermore, advanced neural network modules and more real-world datasets should be considered for integration into the prediction model.  
In this paper, [PERSON] conducted the project administration, methodology, software, and original writing; [PERSON] conducted the data curation and validation; and [PERSON] developed the methodology for the factor analysis and performed the formal analysis, and review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Funding (CN), grant number 41901396, and Youth Innovations Science and technology support project in Colleges of Shandong Province, grant 2021 KJ058. The APC was funded by National Natural Science Funding (CN).  
Not applicable.  
The traffic-flow datasets PeMSD04 and PeMSD08 are open to the public and collected from the California Department of Transportation's Performance Measurement System.  
The authors would like to thank the support from the National Natural Science Foundation of China and the data support from [PERSON] and Shandong Hi-speed Company Limited.  
The authors declare no conflict of interest.