## 5 Experiments
### Result Analysis  
Table 1 presents the performance evaluation results for the proposed model and the baseline experiments on the AVI trajectory reconstruction task using the AVI dataset in Chongqing. Spa refers to the spatial trajectory reconstruction method proposed in  
Section 4.1, while Attn refers to the time series prediction method. It can be observed that the proposed methods, including the spatial reconstruction method and the time prediction method, perform well and have significant advantages over other methods. The best-performing methods in the table are highlighted in bold, while the best experimental results among the traditional methods are marked with underlines. From the experimental results, it can be seen that when the time prediction method is fixed and different spatial reconstruction methods are analyzed for their impact on spatiotemporal reconstruction, the RICK method performs better than MPR and HMM in the experiments, while MPR is slightly better than the HMM method. Compared with these traditional methods, the proposed model performs better, which is consistent with the experimental results. When the spatial reconstruction method is fixed, and different time prediction methods are used for trajectory reconstruction, it can be found that, among the traditional methods, the history method performs slightly better than the linear method. On this basis, the deep learning-based methods RNN and LSTM, which consider the spatiotemporal patterns within the trajectory, are better than the history and linear methods, and the LSTM is better than the RNN in capturing the long-term dependencies within the trajectory. The time series prediction method proposed in this section achieves the best experimental results because it can not only consider the spatiotemporal dependencies within the trajectory but also learn the spatiotemporal relations from the historical trajectory. Furthermore, compared with the traditional methods, the proposed time series prediction model shows an average improvement of 3.59% in the STLCSS index and an average improvement of 7.81% in the STLC index. Additionally, the proposed model outperforms the best-performing traditional method, RICK+LSTM, in spatiotemporal reconstruction, with an improvement of 6.87% in the STLCSS index and an improvement of 9.77% in the STLC index. Although the improvements in the STLC and STLCSS metrics achieved by the proposed method may appear incremental compared to the traditional methods, they are statistically significant given the complexity and sparsity of AVI data. These improvements demonstrate the method's ability to better capture spatiotemporal relationships, which is critical for applications where high accuracy is required, such as security monitoring and traffic analysis. Beyond numerical improvements, the proposed method offers several \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spatial** & **Temporal** & **STLC** & **STLCSS** \\ \hline \multirow{4}{*}{RICK} & Linear & 0.6130 & 0.3196 \\  & History & 0.6207 & 0.3127 \\  & RNN & 0.6797 & 0.2830 \\  & LSTM & 0.6815 & 0.2773 \\  & Attn (ours) & 0.7228 & 0.2425 \\ \hline \multirow{4}{*}{MPR} & Linear & 0.6057 & 0.3249 \\  & History & 0.6187 & 0.3201 \\  & RNN & 0.6486 & 0.2981 \\  & LSTM & 0.6515 & 0.2817 \\  & Attn (ours) & 0.7155 & 0.2491 \\ \hline \multirow{4}{*}{HMM} & Linear & 0.6056 & 0.3381 \\  & History & 0.6159 & 0.3370 \\ \cline{1-1}  & RNN & 0.6332 & 0.3041 \\ \cline{1-1}  & LSTM & 0.6376 & 0.2988 \\ \cline{1-1}  & Attn (ours) & 0.6857 & 0.2619 \\ \hline \multirow{4}{*}{Spa} & Linear & 0.6617 & 0.2710 \\  & History & 0.6645 & 2658 \\ \cline{1-1}  & RNN & 0.6926 & 0.2501 \\ \cline{1-1}  & LSTM & 0.7071 & 0.2478 \\ \cline{1-1}  & Attn (ours) & **0.7681** & **0.2086** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different models.