# Hybrid Graph Models for Traffic Prediction
## 6 Experimental Results
### Predictive Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Data** & **Method** & \multicolumn{3}{c}{**15 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} \\  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{9}{*}{PeMS} & HA & 2.88 & 5.59 & 6.80\% & 2.88 & 5.59 & 6.80\% & 2.88 & 5.59 & 6.80\% \\  & ARIMA & 1.62 & 3.30 & 3.50\% & 2.33 & 4.76 & 5.40\% & 3.38 & 6.50 & 8.30\% \\  & SVR & 1.85 & 3.59 & 3.82\% & 2.48 & 5.18 & 5.50\% & 3.28 & 7.08 & 8.00\% \\  & FC-LSTM & 2.05 & 4.19 & 4.80\% & 2.20 & 4.55 & 5.20\% & 2.37 & 4.96 & 5.70\% \\  & DCRNN & 1.38 & 2.95 & 2.90\% & 1.74 & 3.97 & 3.90\% & 2.07 & 4.74 & 4.90\% \\  & STGCN & 1.36 & 2.96 & 2.90\% & 1.81 & 4.27 & 4.17\% & 2.49 & 5.69 & 5.79\% \\  & Graph WaveNet & 1.30 & 2.74 & 2.73\% & 1.63 & 3.70 & 3.67\% & 1.95 & 4.52 & 4.63\% \\  & ASTGCN & 1.53 & 3.13 & 3.22\% & 2.01 & 4.27 & 4.48\% & 2.61 & 5.42 & 6.00\% \\  & MTGCN & 1.32 & 2.79 & 2.77\% & 1.65 & 3.74 & 3.69\% & 1.94 & 4.49 & 4.53\% \\  & GMAN & 1.34 & 2.82 & 2.81\% & 1.62 & 3.72 & 3.63\% & 1.86 & 4.32 & 4.31\% \\  & MRA-DGCN & 1.28 & 2.75 & 2.68\% & 1.59 & 3.62 & 3.60\% & 1.87 & 4.33 & 4.42\% \\  & HGM (Ours) & **1.25** & **2.66** & **2.64\%** & **1.54** & **3.57** & **3.47\%** & **1.85** & **4.31** & **4.29\%** \\ \hline \multirow{2}{*}{Data} & \multirow{2}{*}{Method} & \multicolumn{3}{c}{15 min} & \multicolumn{3}{c}{30 min} & \multicolumn{3}{c}{60 min} \\  & & MAE & RMSE & MAPE & MAE & RMSE & MAPE & MAE & RMSE & MAPE \\ \hline \multirow{9}{*}{METR-LA} & HA & 4.16 & 7.80 & 13.00\% & 4.16 & 7.80 & 13.00\% & 4.16 & 7.80 & 13.00\% \\  & ARIMA & 3.99 & 8.21 & 9.60\% & 5.15 & 10.45 & 12.70\% & 6.90 & 13.23 & 17.40\% \\  & SVR & 3.99 & 8.45 & 9.30\% & 5.05 & 10.87 & 12.10\% & 6.72 & 13.76 & 16.70\% \\  & FC-LSTM & 3.44 & 6.30 & 9.60\% & 3.77 & 7.23 & 10.90\% & 4.37 & 8.69 & 13.20\% \\  & DCRNN & 2.77 & 5.38 & 7.30\% & 3.15 & 6.45 & 8.80\% & 3.60 & 7.60 & 10.50\% \\  & STGCN & 2.88 & 5.74 & 7.62\% & 3.47 & 7.24 & 9.57\% & 4.59 & 9.40 & 12.70\% \\  & Graph WaveNet & 2.69 & 5.15 & 6.90\% & 3.07 & 6.22 & 8.37\% & 3.53 & 7.37 & 10.01\% \\  & ASTGCN & 4.86 & 9.27 & 9.21\% & 5.43 & 10.61 & 10.13\% & 6.51 & 12.52 & 11.64\% \\  & MTGCN & 2.69 & 5.18 & 6.86\% & 3.05 & 6.17 & 8.19\% & 3.49 & 7.23 & 9.87\% \\  & GMAN & 2.80 & 5.55 & 7.41\% & 3.12 & 6.49 & 8.73\% & 3.44 & 7.35 & 10.07\% \\  & MRA-DGCN & 2.62 & 5.14 & 6.68\% & **3.01** & 6.13 & **8.05\%** & 3.38 & 7.22 & 9.98\% \\  & HGM (Ours) & **2.58** & **5.06** & **6.60\%** & 3.04 & **6.10** & 8.19\% & **3.35** & **7.03** & **9.65\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods on METR-LA and PeMS datasets. on different graph structures. Compared to Graph WaveNet and GMAN, the proposed method simultaneously considers shallow and potential relationships between nodes through a dynamic and static adjacency matrix. The importance of adjacent nodes can be highlighted through different network topology information and the hidden relationship between nodes can be captured in the proposed method. On the METR-LA dataset, the HGM reduces the RMSE metric by 0.36 and 0.42 over 12 future time steps compared to Graph WaveNet and GMAN, respectively.