# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Results and Discussion
#### 3.4.3 Comparative Analysis of Different Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**10 Min**} & \multicolumn{3}{c}{**15 Min**} & \multicolumn{3}{c}{**30 Min**} \\ \cline{2-10}  & **MAE** & **RMSE** & **WMAPE** & **MAE** & **RMSE** & **WMAPE** & **MAE** & **RMSE** & **WMAPE** \\ \hline HA [7] & 31.57 & 56.50 & 21.53\% & 58.59 & 104.87 & 26.55\% & 178.53 & 311.32 & 38.96\% \\ CNN [17] & 22.40 & 33.46 & 15.23\% & 26.54 & 42.20 & 12.03\% & 47.00 & 71.44 & 10.74\% \\ GRU [16] & 20.64 & 31.94 & 14.04\% & 26.39 & 41.15 & 11.94\% & 44.33 & 69.85 & 10.14\% \\ BiGRU [30] & 21.72 & 33.30 & 14.74\% & 28.28 & 43.29 & 12.80\% & 45.28 & 71.31 & 10.35\% \\ TCN [31] & 21.29 & 32.25 & 14.50\% & 26.41 & 40.76 & 11.97\% & 43.52 & 66.53 & 9.95\% \\ ResLSTM [32] & 20.10 & 32.08 & 13.68\% & 24.13 & 39.17 & 10.96\% & 40.70 & 65.89 & 9.31\% \\ Conv-GCN [26] & 18.11 & 29.75 & 12.24\% & 23.41 & 39.22 & 10.68\% & 40.20 & 64.75 & 9.34\% \\ MRDSCNN & 16.91 & 27.74 & 11.53\% & 21.84 & 35.99 & 9.89\% & 37.83 & 61.75 & 8.65\% \\ STGCN [33] & 16.74 & 30.36 & 11.30\% & 25.11 & 45.54 & 16.95\% & 50.22 & 91.08 & 33.90\% \\ DCRNN [33] & 18.00 & 32.24 & 12.14\% & 27.00 & 48.36 & 18.21\% & 54.00 & 96.72 & 36.42\% \\ GBDT [22] & 20.59 & 34.33 & - & 30.88 & 51.50 & - & 36.48 & 61.94 & - \\ TSTFN [34] & - & - & - & 23.56 & 39.70 & - & 47.12 & 79.40 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different models for passenger inflow. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**10 Min**} & \multicolumn{3}{c}{**15 Min**} & \multicolumn{3}{c}{**30 Min**} \\ \cline{2-10}  & **MAE** & **RMSE** & **WMAPE** & **MAE** & **RMSE** & **WMAPE** & **MAE** & **RMSE** & **WMAPE** \\ \hline HA [7] & 50.34 & 104.47 & 22.32\% & 74.24 & 154.63 & 27.41\% & 146.55 & 303.63 & 35.73\% \\ CNN [17] & 25.50 & 39.03 & 17.35\% & 27.18 & 42.06 & 12.28\% & 48.09 & 73.95 & 10.85\% \\ GRU [16] & 23.48 & 36.49 & 15.94\% & 26.76 & 41.40 & 12.06\% & 48.02 & 72.86 & 10.79\% \\ BiGRU [30] & 23.96 & 37.35 & 16.27\% & 27.21 & 41.95 & 12.22\% & 45.48 & 72.15 & 10.24\% \\ TCN [31] & 23.85 & 36.68 & 16.22\% & 26.98 & 41.54 & 12.19\% & 41.20 & 65.10 & 9.30\% \\ ResLSTM [32] & 21.57 & 35.78 & 14.69\% & 25.17 & 40.55 & 11.41\% & 38.30 & 65.44 & 8.64\% \\ Conv-GCN [26] & 20.31 & 33.12 & 13.82\% & 24.01 & 39.02 & 10.82\% & 38.41 & 64.62 & 8.65\% \\ MRDSCNN & 19.75 & 31.27 & 13.48\% & 23.83 & 38.59 & 10.73\% & 37.46 & 62.49 & 8.45\% \\ STGCN [33] & 20.25 & 34.72 & 13.68\% & 30.38 & 52.08 & 20.52\% & 60.76 & 104.16 & 41.04\% \\ DCRNN [33] & 21.77 & 37.84 & 14.69\% & 32.66 & 56.76 & 22.04\% & 65.32 & 113.52 & 44.07\% \\ MGSTCN [35] & - & - & - & 24.10 & 36.50 & - & 48.20 & 73.00 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different models for passenger outflow. Figure 17: Comparison of model performance at different time granularities. (**a**) Prediction performance of passenger inflow; (**b**) Prediction performance of passenger outflow.