## 4 Conclusions  
As far as we know, most existing studies mainly focused on spatio-temporal feature extraction to construct a multi-station passenger flow prediction model by using different deep learning models. Different from the previous studies, we have proposed a novel two-step strategy, namely classification followed by prediction, to develop a better  
Figure 7: Inflow and outflow at 1 st Wharf Station in the Xiamen BRT on Tomb Sweeping Day.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multirow{2}{*}{**Prediction Mode**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-5}  & & **MAE** & **RMSE** & **WMAPE** \\ \hline \multirow{2}{*}{10 min} & Prediction (with classification) & **9.2998** & **15.6633** & **18.58\%** \\  & Prediction (w/o classification) & 9.3990 & 16.0151 & 18.80\% \\ \multirow{2}{*}{15 min} & Prediction (with classification) & **12.7193** & **22.2835** & **16.29\%** \\  & Prediction (w/o classification) & 12.7559 & 22.3270 & 16.33\% \\ \multirow{2}{*}{30 min} & Prediction (with classification) & 23.0463 & **46.2256** & 14.26\% \\ \multirow{2}{*}{10 min} & Prediction (w/o classification) & **22.6983** & 47.2300 & **14.03\%** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction results in the Xiamen BRT.  
performance model for multi-station passenger flow prediction. Two different complex real-world ridership datasets have been used to demonstrate the effectiveness of the proposed model. Compared with the unclassified results, the proposed model (Transformer-K-Means)-(ResNet-GCN-AttLSTM) with station classification presents a better performance in multi-station passenger flow prediction. As far as we know, this is the first time station classification has been added into multi-station passenger flow prediction, which presents good performance.  
Improvements can be made in future work. One issue is in developing a more advanced multi-station passenger flow prediction model by better extracting the complex spatio-temporal features, such as using the fractal-wavelet modeling [30; 31; 32; 33; 34; 35]. Another one is in combining the classification block with more state-of-the-art models to verify the effectiveness of station classification in multi-station passenger flow prediction.  
The authors' contributions are summarized below. [PERSON] was involved in drafting the manuscript; [PERSON] performed the experiments and was involved in drafting the experimental part of the manuscript; [PERSON] and [PERSON] gave a lot of effective suggestions for an improved method and modified the manuscript; [PERSON] provided the idea of the proposed method. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the National Natural Science Foundation of China, grant number 62103345; and Natural Science Foundation of Fujian Province, grant number 2020H0023 and 2020J02160; and the Youth Innovation Fund Project of Xiamen, grant number 3502Z20206076; and the High-Level Talents Research Launched Project of Xiamen University of Technology, grant number YKJ19012R. All the projects are related to the multi-station passenger flow prediction.  
Not applicable.  
Many thanks to Key Laboratory of Fujian Universities for Virtual Reality and 3D Visualization, which supports the data preprocessing. The authors would like to thank the anonymous reviewers and the editors for all the helpful suggestions.  
The authors declare no conflict of interest.