## 6 Conclusions and the Future Work  
In the present study, we introduce ADDGCN, a pioneering attention-fused down-sampling dynamic spatio-temporal graph neural network model, designed for traffic forecasting tasks. Specifically, we incorporate dynamic graph convolution into the interactive learning architecture of down-sampled convolutional networks with the aim of simultaneously capturing spatio-temporal dependencies. In addition, we put forward a dynamic graph convolution network to simulate dynamic spatial correlation. The DGCN can discover invisible node relationships in road networks, thereby capturing hidden spatial relationships. Experiments on two publicly available traffic datasets indicate that ADDGCN performs better than cutting-edge baselines. Accurate traffic flow prediction is crucial for optimizing urban traffic management, reducing congestion, and enhancing road safety. It also assists individuals in planning their travel, improves the efficiency of public transportation, and provides data support for urban planning and environmental policies.  
It is essential to acknowledge that traffic flow is profoundly impacted by diverse elements, including meteorological conditions and holidays. In our forthcoming research, we plan to integrate these influencing factors into the model to enhance its prediction accuracy. By considering weather-related variables and the impact of holidays and special events on traffic patterns, our models will be better suited for real-world scenarios and yield more precise forecasts. Moreover, we recognize that certain parameters in the current models are empirically defined and lack a strong theoretical foundation. To address this limitation and enhance the model's overall performance, we intend to explore the incorporation of optimization algorithms. By employing advanced optimization techniques, we can fine-tune the model's parameters, resulting in a more data-driven and accurate forecasting process. This approach will enable us to discover more optimal parameter settings, better capturing the complex relationship between traffic flow patterns and diverse influencing variables.  
**Author Contributions:** Conceptualization, C.W. and Z.L.; methodology, H.W. and Z.L.; software, S.W.; validation, Z.L., H.W. and C.W.; formal analysis, S.W.; investigation, Z.L. and C.W.; resources, H.W.; data curation, C.W.; writing--original draft preparation, C.W. and Z.L.; visualization, H.W.; supervision, C.W.; project administration, C.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2023 Provincial Science and Technology Plan Project 2023 BCB041 \"Research and Application of Core Technologies for Intelligent Energy Saving in 5G Transmission Network Sites\", and 2023 BAB022 \"Research and Application of Cross-Road Domain Traffic Flow State Recognition and Migration Technology Based on Spatio-Temporal Data-Driven\".  
The authors approve that the data used to support the findings of this study are included in the article. The data used in the article can be downloaded from the following link: [[https://github.com/divanoresia/Traffic](https://github.com/divanoresia/Traffic)]([https://github.com/divanoresia/Traffic](https://github.com/divanoresia/Traffic)) (accessed on 20 May 2022).  
Author [PERSON] was employed by the company CCCC Second Highway Consultants Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.