## 5 Discussion and Conclusions  
This study presents a finely resolved subway station model using Anylogic passenger simulation. Additionally, we propose a novel spatial-temporal graph neural network named STGNN tailored for passenger flow prediction tasks in metro stations. Based on this, new modules for spatial and temporal dimensions are redesigned to capture respective spatial and temporal dependencies. Specifically, we devise a dynamic global attention network to discern and adaptively capture the dynamic global influence along the spatial dimension, thereby facilitating the exploration of passenger flow fluctuation between global node pairs. The GCN integrated into the spatial modeling module incorporates pertinent prior information derived from the input graph structure. Furthermore, we introduce specialized modules such as the series decomposition block and locality-aware sparse attention block designed for time series prediction. The series decomposition block decomposes the passenger flow series into seasonal and trend-cyclical components, helping the model more accurately capture the seasonality and progression of the passenger flow series. The locality-aware sparse attention block aims to extract multiple local contexts of information and alleviate the computational complexity associated with long sequence modeling, contributing to more accurate passenger flow predictions and reduced inference time. Through comparative experiments and ablation experiments on the simulation dataset and Hangzhou dataset, we demonstrate the effectiveness and robustness of our proposed approach. Nevertheless, the STGNN model has limitations: (1) The intricate patterns of fluctuation and the spatial-temporal relationships inherent in metro passenger flow remain subject to the influence of numerous unknown external events, such as weather conditions. Our model can only implicitly learn the impact of these unknown external events on passenger flow evolutions and cannot accurately assess the influence of any specific external factor. (2) Our model analyzes passenger flow variations by aggregating long-term and recent-term passenger flow segments, where the long-term segments only include daily-period passenger flow while ignoring weekly-period passenger flow variations. In future work, we will enhance the applicability and generalization capability of STGNN from the following aspects: (1) We plan to adjust the components of the STGNN to generalize it to other prediction tasks. (2) We plan to construct the learning of spatial correlations of passenger flow between metro stations from a multi-graph perspective. (3) Future research could incorporate multi-source data, such as weather forecasts, holidays, and metro schedules, to enhance the model's robustness. These will help optimize metro schedules, enhance passenger experience, and support data-driven decision-making in infrastructure development. Y.C. : Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing--original draft, Writing--review and editing. M.Z. : Formal analysis, Any logic Software, Writing--review and editing. Y.D. : Methodology, Writing--review and editing. K.W. : Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing--original draft, Funding acquisition, Supervision, Writing--review and editing. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Computation Time for Training Epoch (s)**} \\ \cline{2-3}  & **Prediction Step = 30** & **Prediction Step = 60** \\ \hline STG-NODE & 119 & 209 \\ ASTGCN & 11.49 & 27.81 \\ ASTGNN & 18.33 & 33.31 \\ STGNN & 8.59 & 13.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The computation time for the training epoch. * [1] [PERSON]; [PERSON] Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. _Transp. Res. Part C Emerg. Technol._**2012**, _21_, 148-162.