# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 5 Conclusions  
In this research, we introduce an STFEformer model designed to improve the understanding of complex traffic data characteristics for traffic flow prediction. Specifically, we introduce a uniquely designed fusion embedding layer to efficiently extract and fuse multi-perspective features, including spatial, temporal (short-term and periodic), and native features in traffic data in order to obtain a more comprehensive representation of traffic data features and lay a solid foundation for accurate traffic flow prediction. We designed a novel spatial self-attention module that leverages a mask matrix method to enhance the detection of dynamic, long-range spatial correlations. To further explore dynamic temporal correlations, a temporal self-attention module is also employed. We conducted extensive experiments to evaluate our model on three real-world public datasets, in which it outperformed twelve baseline models. The ablation experiments, in particular, provide crucial insights into the effectiveness of each component, affirming the significant contribution of our novel mechanisms to the model's overall performance. Finally, we visualized the prediction results and the learned adaptive similarity matrices, significantly enhancing the interpretability and transparency of our model. These visualizations not only confirm the accuracy of our predictions but also provide intuitive insights into the model's decision-making process. In the future, we intend to integrate STFEformer with other computational methods such as deep-learning algorithms for real-time data analysis and apply our model  
Figure 9: Heat maps of the similarity matrix for (**a**) PeMS04, (**b**) PeMS07, and (**c**) PeMS08.  
to diverse contexts, such as smart city planning and autonomous vehicle navigation, in order to delve deeper into the hidden information in spatial-temporal data and discover the impact of spatial-temporal dimensions on prediction accuracy. By pursuing this way, our objective is to further improve the predictive performance of STEformer and discover its potential applications in different tasks. Moreover, we will further investigate mechanisms that can dynamically update traffic networks to reflect real-time changes in traffic patterns.  
Conceptualization, H.Y. and Y.W.; methodology, H.Y.; validation, H.Y. and S.W.; formal analysis, H.Y.; data curation, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, Y.W.; supervision, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Basic Research Program of Shaanxi (grant no. 024 JC-YBQN-0395) and the 111 project of Sustainable Development of Transportation in Western Urban Agglomeration (grant no. B20035).  
Not applicable.  
The data used in the experiments in this work are included in the text or can be found in the references provided in the article.  
The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions. The authors wish to acknowledge the contributions of [PERSON], who provided validation and supervision for this research. Her insights and oversight were invaluable to the successful completion of this research.  
The authors declare no conflicts of interest.