# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Results and Discussion
#### 3.4.3 Comparative Analysis of Different Models  
In this experiment, the input passenger inflow and outflow data are entered into all models. When analyzing the results of the performance experiments based on the above three metrics (as shown in Figures 15 and 16), we found a clear trend that the MRDSCNN model consistently outperforms other models in both passenger inflow and outflow predictions. As shown in Tables 6 and 7, deep learning methods perform better than traditional models in predicting metro passenger flow at different time granularities. The traditional statistical approach HA performs the worst because of its limited ability to capture complex spatiotemporal dynamics through fitted regression. The second-worst model is the deep learning model CNN, which does not perform better than the other six deep learning methods, mainly because its fixed receptive field does not allow for the extraction of global information. Then, we can see that both GRU and BiGRU perform better than CNN. This indicates that the recurrence-based approaches can model long-term dependencies in sequential data, allowing them to capture and update crucial information from past time steps. Unlike the single model described above, TCN, as an ensemble model, can expand the receptive field and capture multi-scale features through the residual structure. However, ResLSTM has a better performance than TCN because ResLSTM constitutes a multifaceted architecture influenced by intricate factors, where the GCN component is an important part of processing graph structure data. Furthermore, as the best baseline model in this paper, Conv-GCN integrates the inflow and outflow information to extract high-level spatiotemporal features between near and far stations. Although all the models demonstrate small prediction performance gaps when the time granularity is 10 min, MRDSCNN begins to show its superior prediction performance compared to other baseline models as the time granularity increases. It is worth mentioning that our proposed model integrates multiple graphs to cover the spatiotemporal complexity inherent in different inter-station connections, resulting in superior prediction accuracy and the best model fit. Figure 16: Comparison of model performance for passenger outflow. (**a**) MAE; (**b**) RMSE; (**c**) WMAPE. Figure 15: Comparison of model performance for passenger inflow. (**a**) MAE; (**b**) RMSE; (**c**) WMAPE. Moreover, we also compared the experimental results of models STGCN [33], DCRNN [33], GBDT [22], TSTFN [34], and MGSTCN [35] with the Hangzhou Metro dataset, which indicated that the proposed model has better predictive performance. We also assessed the performance of the proposed model at different time granularities, as illustrated in Figure 17. Remarkably, we observed that as the time granularity increases, both the MAE and RMSE rise while the WMAPE decreases. This is mainly because the influence of short-term fluctuations on passenger flow is reduced when the time granularity is from 10 to 30 min, making it easier to capture general trends and patterns of passenger flow.