## 4 Results
### Results of Path Completion  
Among the 1000 car mode data records, we complete 994 paths, of which 953 paths are correctly identified as car mode, which achieves an accuracy rate of 95.3% in recognizing car mode. But 41 paths are wrongly identified as the public transit mode. The potential reason for the misidentification of travel modes is that for these travel records, the time difference in travel duration between choosing private car mode and choosing public transportation during the departure and arrival time periods is not very significant.  
In the case of 1000 public transit mode data records, we identify the public transit mode for only 616. One possible reason is that we may mistakenly identify many public transportation trips with long first-/last-mile distances as the car mode. The first-/last-mile distance can influence people's choice of public transit mode. To address such an issue, we specify a radius of 1000 m for searching nearby stations of each trip's origin and destination. Hence, public transit trips with excessive walking distances before and after boarding and lengthy transfer distances are excluded.  
The performance of the three path completion methods across ten testing sets is shown in Figure 9. The accuracy of all three methods decreases with the increase in missing path numbers. As travel routes become longer (involving more links), the transportation system's uncertainty increases, making it difficult for travelers to perceive and choose the shortest path. In other words, the shortest-path completion methods face challenges when simulating human behavior on long-distance trips. Therefore, the accuracy of the shortest path algorithm is highly sensitive to the completeness of the data.  
After separately weighting the quantities and lengths of missing road segments, Table 6 presents the comprehensive LCR and PLCR values for the DDSM and two baseline  
\\begin{table}
\\begin{tabular}{c c c c c} \\hline \\hline
**Time Period** & **Initial RMSE** & **Final RMSE** & **Initial NRMSE** & **Final NRMSE** \\\\ \\hline
1 & 4.117 & 1.951 & 0.442 & 0.209 \\\\
2 & 4.760 & 1.912 & 0.572 & 0.230 \\\\
3 & 5.071 & 2.156 & 0.649 & 0.276 \\\\
4 & 5.379 & 2.106 & 0.732 & 0.287 \\\\
5 & 5.510 & 1.718 & 0.724 & 0.226 \\\\
6 & 5.440 & 1.756 & 0.722 & 0.233 \\\\
7 & 5.424 & 2.063 & 0.670 & 0.255 \\\\
8 & 4.974 & 1.801 & 0.618 & 0.224 \\\\ All & 5.217 & 1.939 & 0.638 & 0.241 \\\\ \\hline \\hline \\end{tabular}
\\end{table}
Table 5: Comparison of RMSE and NRMSE between simulated and real-world space mean speeds of the target links before and after calibration.  
Figure 9: Comparative analysis of LCR and PLCR in path completion algorithms across ten testing sets: (**a**) comparative evaluation of LCR among diverse path completion algorithms; (**b**) comparative evaluation of PLCR among diverse path completion algorithm.  
algorithms, as well as the averages of LCR and PLCR. The numbers outside the parentheses represent the average metrics derived from 10 subsets, while the numbers inside the parentheses indicate the standard deviations for these subsets.  
Compared to two comparative algorithms, the LCR values of DDSM separately increase by 6.80% and 2.82%, while the PLCR values increase by 5.83% and 2.10%, respectively. Furthermore, the comprehensive averages of LCR and PLCR for DDSM improved by 6.31% and 2.46%, respectively, indicating its overall superiority. Notably, the performance of DDSM is even more stable across all ten subsets, further validating its robustness and reliability. This stability across multiple subsets underscores the significant advantage of the proposed DDSM in terms of accuracy, making it a promising approach for missing path completion.