## 6 Visualization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Keywords** & **Max** & **Min** & **Mean** \\ \hline \multirow{4}{*}{DDPG} & Q Loss & 4.93 & 0.89 & 3.19 \\  & A Loss & 0.58 & \(-\)2.61 & \(-\)1.24 \\  & MAE & 703.83 & 120.30 & 398.27 \\  & RMSE & 77.23 & 34.68 & 58.12 \\ \hline \multirow{4}{*}{HRA} & Q Loss & 4.12 & 1.70 & 2.59 \\  & A Loss & 1.13 & 0.67 & 0.84 \\  & MAE & 161.49 & 87.88 & 115.93 \\  & RMSE & 39.95 & 25.82 & 30.69 \\ \hline \multirow{4}{*}{HRP} & Q Loss & 8.07 & 3.56 & 4.72 \\  & A Loss & 2.52 & 1.91 & 2.05 \\  & MAE & 151.24 & 54.05 & 69.65 \\  & RMSE & 38.89 & 18.34 & 28.02 \\ \hline \multirow{4}{*}{STRPA} & Q Loss & 3.21 & 1.21 & 1.81 \\  & A Loss & 2.17 & 1.59 & 1.81 \\ \cline{1-1}  & MAE & 165.15 & 28.43 & 60.29 \\ \cline{1-1}  & RMSE & 39.02 & 16.87 & 30.64 \\ \hline \multirow{4}{*}{STRPA-A} & Q Loss & 5.33 & 1.93 & 3.27 \\ \cline{1-1}  & A Loss & 1.77 & 1.14 & 1.36 \\ \cline{1-1}  & MAE & 188.64 & 51.73 & 70.53 \\ \cline{1-1}  & RMSE & 48.34 & 25.40 & 30.71 \\ \hline \end{tabular}
\end{table} 
Table 1: The performance of five models. _Q Loss_ indicates the loss value from the critic module; _A Loss_ indicates the loss value from the actor module; MAE indicates mean absolute error between prediction and targets; RMSE indicates mean root mean squared error between prediction and targets. Graph convolution network can mainly aggregate the information of the neighborhood nodes through the adjacency matrix, so the different adjacency matrices result in different impacts of certain models. It can be seen from Section 5.4 that there are four kinds of matrices, including NAu, NAw, PAu, and PAw. The STRPA model with four matrices is selected to explore the influence of the matrix. It can be seen from the three charts that the performance of the model with different matrices is excellent, respectively, in different periods. In general, the performance of the unweighted matrix is better than that of the weighted matrix, and the performance of the neighborhood adjacency matrix is better than that of the pattern adjacency matrix. The unweighted matrix is more accessible to obtain than the weighted matrix, and it can minimize the difficulty of the model. The reason for the better performance of the neighborhood matrix is that the node relationship assumed to influence detour decisions is more likely the neighbor relationship constructed by the geospatial structure than the patterned relationship constructed by the similarities between node characteristics. From this, we can see that the construction of our matrix should be closer to the physical meaning of the model. In Figures 8a,b and 9, we show the reward curves of three models in the early, middle, and late periods. It can be seen that the STRPA model is the best among the three models in terms of the stable performance of reward and the fluctuation of the curve, and we can see that the performance of the three models in the same period has little difference in reward. On the contrary, the performance difference of the same model in different periods is a little larger. This is due to the degree of internal imbalance of the bicycle network, which is independent of the model itself. Figure 8: The curve diagram of loss from STRPA using different adjacency matrices. Matrices represent different node relationships during the different time. (**a**) The MAE values in different adjacency matrices; the performance of matrix \(NAu\) is best. (**b**) The RMSE value in different adjacency matrices; the performance of matrix \(NAu\) is best. Figure 7: The MAE and RMSE of STGRA with different components. The MAE values of STRPA, STRPA-A, STRPA-C in different time on left subfigure. The RMSE values of STRPA, STRPA-A, STRPA-C in different times on right subfigure. The dashed line represents the mean value of all results.