# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 5 Experiments
### Experiments Results  
We first compare our model with all the baselines above. To ensure the comparison is fair, all the deep learning models were re-implemented to add our graph embedding module, and then we compared the improved baselines with our model. Here, we present the results of these two datasets.  
The results from Table 3 show that our model achieves start-of-the-art performance in all benchmarks, and several other observations can be made in this table.  
Firstly, deep learning methods achieve higher performance than traditional machine learning methods on both datasets. The result of the STD shows that the positioning accuracy of the signaling data is relatively low. Secondly, bidirectional models perform better than undirected ones, which indicates the importance of the spatial-temporal features of the model; this result also showed that the past and future mobility patterns can help locate the exact locations of users. Thirdly, transformer-based models perform better than MLP-based or LSTM-based models, which shows the fact that the self-attention mechanism greatly improves the performance and transformer-based models can be applied to more time-series scenarios. Moreover, TSMixer performs better than transformer-based methods.  
We visualize the results of our work in a more detailed way by showing the rating change with the meter threshold increases and comparing it with some of the baselines in our work on the MSD dataset. To clearly show the figure, we only visualize some of the baselines.  
The results from Figure 4a show that our model improves the performance by increasing the ratio of the error under 100 m for a single location, thus improving the performance of the model. Another result in Figure 4b, which is divided by the distance between the cell towers and real locations, shows that the distance between the matched cell tower and the real position may not be the main reason that affects the performance of our model, since the increase in the distance error between the matched cell tower and the real position does not violently affect the performance of the model.  
To conclude, our model achieves the best results compared with both traditional methods and deep learning methods, which verifies the effectiveness of our model in capturing the spatial-temporal features of the coarse trajectory sequence, and the method gives the possibility of using coarse-grained trajectory to rebuild or generate fine-grained trajectories at any given period.  
\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 STD & 404.488 & 315.976 & 32.308 & 213.438 & 201.535 & 23.707 \\ STD3 & 399.023 & 303.457 & 34.54 & 261.967 & 197.308 & 37.723 \\ LR & 364.962 & 281.36 & 37.008 & 210.91 & 192.55 & 32.376 \\ XGBoost & 339.354 & 209.193 & 50.105 & 157.056 & 137.918 & 58.678 \\ \hline MLP & 238.35 & 178.61 & 56.31 & 259.49 & 190.25 & 46.12 \\ LSTM & 216.49 & 154.97 & 64.83 & 181.17 & 140.85 & 61.36 \\ BiLSTM & 219.18 & 149.24 & 69.48 & 173.43 & 138.51 & 61.6 \\ \hline DeepMove & 192.35 & 135.42 & 72.49 & 171.98 & 135.71 & 63.5 \\ Transformer & 160.87 & 102.89 & 83.33 & 112.01 & 83.32 & 86.69 \\ TSMixer & 158.4 & 99.54 & 83.3 & 108.45 & 80.09 & 87.32 \\ TrajFormer & 153.9 & 92.53 & 85.52 & 109.87 & 79.85 & 87.45 \\ GenTrajRec & **144.58** & **82.3** & **87.85** & **103.45** & **77.12** & **88.36** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The result of GenTrajRec and the baselines.