## 6 Conclusions  
Aiming at the lack of ability of static graphs to model the dynamic spatial association of multiple parking lots, this paper proposed a novel dynamic graph model, DSTAGCRN, for predicting available parking spaces. The proposed model combines node embedding with dynamic graphs to explore the spatial correlation and dynamic information between parking lots in the road network, providing initial evidence of the effectiveness of dynamic graphs in improving prediction performance. We decomposed the original parking data into cyclic trends and seasonal trends, extracting long-term stationary trends from hidden  
Figure 11: Impact of different parameters on the model.  
Figure 10: Impact of different embedding dimensions.  
variables to enhance predictive accuracy. Additionally, we introduced a multihead attention mechanism module that self-adjusts based on the dynamic information at each time step to explore more effective hidden information. Ablation experiments validated the effectiveness of these components, indicating the high potential of the model in exploring the spatio-temporal structure of available parking spaces.  
Compared with existing prediction models, we conducted extensive experiments on three actual datasets, and the results showed that the proposed DSTAGCRN model could more accurately and efficiently model the number of available parking spaces. In these experiments, we used datasets from Singapore, Zurich, and Guangzhou, which covered different urban environments and parking lot types, validating the widespread applicability and robustness of our model. Specifically, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of DSTAGCRN on the test set were significantly lower than those of other benchmark models. For example, compared with the ASTGCN model proposed by [PERSON] et al. [30], our DSTAGCRN model reduced MAE by 0.89, RMSE by 1.47, and MAPE by 4.93% on the Zurich dataset. This advantage is mainly focused on handling dynamic spatial correlations and temporal dependencies. Meanwhile, compared with other benchmark models such as LSTM, MegaCRN, DGCN, etc., it also showed a stronger generalization ability and robustness.  
In future research, we plan to integrate more external factors such as temperature, weather, points of interest (POIs), and traffic accidents into the model to further improve the performance and reliability of parking space prediction. In addition, we also would like to explore the applicability of the model in other cities and more application scenarios to promote the development of smart cities. We believe that through continuous optimization and expansion, the graph neural network model would provide more effective technical support for parking management and traffic optimization.  
Methodology, W.J.; Formal analysis, W.F.; Resources, L.G.; Writing--original draft, W.F.; Writing--review and editing, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Natural Science Foundation (Project No.: 2023J01946) and Fujian Provincial Department of Education Young and Middle-aged Teacher Education Research Project (Project No.: JAT190433).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We would like to express our gratitude to [PERSON] for helping us organize the relevant references.  
The authors declare no conflicts of interest.