# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 5 Experiments
### Ablation Study  
To analyze the effectiveness of our proposed model components, we create the ablation study by removing them one by one. Specifically, we will remove the self-supervised graph embedding part, the feature fusion part and both of these two parts to evaluate the effectiveness of our proposed module. The feature fusion part was removed by replacing it with a single MLP layer, while the self-supervised graph embedding part was removed by using only MSELoss in the optimization part and the graph embedding part. The results are shown in Table 4.  
According to the results, our model outperforms all the ablations, which illustrates the importance of each of the proposed components. The performance drops a lot when we remove the self-supervised graph embedding part since this module can be used to generate area embedding, and the removal of this part also means the removal of the unsupervised learning part. Moreover, we find that the feature fusion part is also helpful for the performance since this part not only contains the raw info but also has the representation of the change of the spatial-temporal features. We also visualize the reconstruction results in a more sophisticated way by visualizing the results of each of the model's results in a more detailed way. Showing the rating of reconstructing with the meter threshold increase from 100 m to 500 m, each time the increase is 100 m, and the results of the MSD are in Figure 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**MSD**} & \multicolumn{3}{c}{**Geolife**} \\ \cline{2-7}  & **RMSE** & **MAE** & **Rate150** & **RMSE** & **MAE** & **Rate150** \\ \hline w/o feature fusion & 160.87 & 102.89 & 83.33 & 112.01 & 83.32 & 86.89 \\ w/o Graph & 161.28 & 103.73 & 81.26 & 150.24 & 124.62 & 67.89 \\ w/o both & 167.32 & 107.92 & 80.52 & 161.56 & 139.06 & 59.57 \\ GenTrajRec & **144.58** & **82.3** & **87.85** & **103.45** & **77.12** & **88.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The result of the ablation. Figure 4: Results for different distances between the matched cell towers and the real positions. Figure 5: Detailed ablation study on MSD dataset. According to the results from Figure 5a, our model performs well in each of the distance ranges. We also find that the graph embedding part performs better than the feature fusion part in the range of 100m. A possible reason is that the graph embedding part helps to locate the position in the coarse area and then predict a possible location in the range of the area, thus improving the positioning accuracy, while the feature fusion part tends to retain the original position information and helps to enhance the positioning accuracy while the base range is longer, as we can see from the calculation from Figure 5b. To further show the effectiveness of the graph embedding part, we also remove the graph embedding part in all the baselines if the original baseline does not contain this part and show the performance of our GenTrajRec in Table 5.  
From Table 5, the results show that the removal of the graph embedding parts causes a decrease in the performance of the baselines. Despite removing the graph embedding part, our GenTrajRec still achieves the best performance among all the baselines. The results also imply that the ID and the latitude and longitude of the cell towers both help with the real position restoration task, and the graph embedding module can be used for more downstream tasks. Case Analysis  
To show that our model can deal with short and long signaling sequences, we visualize some of the cases of our work. To achieve this task, we try to analyze the details of the error distribution of our model.