## 4 Simulation Process and Results of Train Energy-Saving Optimization
### Analysis of Simulation Results
#### 4.3.3 Performance Comparison Test with Other Methods  
In order to comprehensively evaluate the effect of the high-speed rail energy-saving strategy proposed in this paper, based on existing data, comparative experiments are designed to discover the performance differences between the VAGE method and the Jaccard and Adamic-Adar methods in depth. The energy-saving percentage of the traction energy consumption with the same distance interval under the VAGE, Adamic-Adar, and Jaccard methods compared with the original strategy is shown in Table 11.  
The VGAE algorithm saves about 17.68% of the traction power throughout the simulated driving route and shows energy savings earlier than the other algorithms. A comparison of the speed and travel time between the proposed method and other existing methods is shown in Table 12.  
The maximum speed of the VGAE method does not exceed 300 km per hour, and the travel time is shorter than that of the other methods. The experimental results can verify the effectiveness of using a multi-view variational graph autoencoder to solve the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Travel Distance** & **Original Policy** & **Optimization Strategy** & **Energy Saving** & **Energy Saving Percentage** \\ \hline
30,000 m & 989.41 kwh & 962.08 kwh & 27.33 kwh & 2.76\% \\
40,000 m & 1173.89 kwh & 1069.72 kwh & 104.17 kwh & 8.87\% \\
50,000 m & 1339.81 kwh & 1178.25 kwh & 161.56 kwh & 12.06\% \\
60,000 m & 1530.78 kwh & 1285.89 kwh & 244.89 kwh & 15.99\% \\
70,000 m & 1714.92 kwh & 1393.53 kwh & 321.39 kwh & 18.75\% \\
83,786 m & 1747.18 kwh & 1418.98 kwh & 328.2 kwh & 18.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cumulative energy consumption comparison.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Distance**} & **Original** & \multicolumn{2}{c}{**VGAE**} & \multicolumn{2}{c}{**Adamic-Adar**} & \multicolumn{2}{c}{**Jaccard**} \\ \cline{2-7}  & **Consumption** & **Consumption** & **Energy-Saving** & **Consumption** & **Energy-Saving** & **Consumption** & **Energy-Saving** \\ \hline
30,000 m & 989.4 kwh & 984.13 kwh & 0.5\% & 1078.39 kwh & — & 1092.78 kwh & — \\
40,000 m & 1173.89 kwh & 1092.67 kwh & 6.92\% & 1230.19 kwh & — & 1200.42 kwh & — \\
50,000 m & 1339.8 kwh & 1201.27 kwh & 10.34\% & 1337.83 kwh & 0.15\% & 1308.06 kwh & 2.37\% \\
60,000 m & 1530.78 kwh & 1308.84 kwh & 14.5\% & 1445.47 kwh & 5.57\% & 1415.69 kwh & 7.5\% \\
70,000 m & 1714.92 kwh & 1417.38 kwh & 17.35\% & 1553.11 kwh & 9.44\% & 1511.43 kwh & 11.87\% \\
83,786 m & 1747.18 kwh & 1438.26 kwh & 17.68\% & 1591.89 kwh & 8.89\% & 1519.44 kwh & 13.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Energy saving percentage under different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Figure Correlation** & **Using GNN** & \multirow{2}{*}{**Travel Time**} & \multirow{2}{*}{**Maximum Speed**} \\  & **(\(\times\) Represents That the** & & **(\(\times\) Represents That the** & & \\  & **Method Doesn’t Use It, and \(\surd\)** & **Method Doesn’t Uses It, and \(\surd\)** & **Represents the Opposite)** & \\ \hline Original & \(\times\) & & \(\times\) & 23 min 54 s & 304.09 km/h \\ Adamic-Adar & \(\surd\) & \(\times\) & 23 min 58 s & 299.42 km/h \\ Jaccard & \(\surd\) & \(\times\) & 24 min 30 s & 299.82 km/h \\ VGAE & \(\surd\) & \(\surd\) & 22 min 41 s & 299.75 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 12: The comparison of speed and travel time.  
energy-saving problem of high-speed trains, that is, using less energy in a shorter period of travel.