## 5 Conclusions  
This study proposes a dual-driven simulation model (DDSM) based on LBS data and traffic simulation to achieve multi-modal simulation modeling and calibration as well as missing LBS data completion. The DDSM approach explores and utilizes LBS data, a novel but underused data source in simulation model development and calibration. Relying on the route/mode choice results from the calibrated model, the missing paths of LBS data are completed by matching the departure and arrival times and locations of simulated trip paths and those of observed trips. The applicability of this method was validated by applying it to an urban area in Hangzhou, China. When utilizing an LBS data and calibration framework in our paper, the model error quickly falls within the permissible range (24%). The results demonstrate the excellent performance of the proposed approach in identifying private car travel modes, achieving an accuracy rate of 95.3%, and outperforming baseline LBS completion algorithms with respective increases of 6.31% and 2.46% in completion accuracy. According to the case study, the completion process adheres to the real-world average road speeds, making it more realistic, and thus the identification of travel modes and paths has a high accuracy. This implies the advantages of our DDSM method in urban-level traffic modeling and the large-scale completion of missing LBS data.  
Future work should focus on improving the following aspects of implementation. Due to data limitations, the calibration process used Space Mean Speed for calibration. Subsequent research can use more abundant road condition information (e.g., traffic flow, and speed) for a more precise simulation model calibration. Due to the lack of public transport trip records and route selection in the data itself, we can only output possible solutions for public transit route selection through the model, making a rough screening using departure and arrival time. In the future, other data sources will be used for further testing and verification.  
Methodology, H.W., Z.S., Y.C., Z.Z. and X.C.; Validation, H.W.; Formal analysis, H.W., Y.C., Z.Z. and X.C.; Data curation, H.W. and Y.C.; Writing--original draft, H.W. and Z.S.; Writing--review & editing, H.W., Z.S., Z.Z. and X.C.; Visualization, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang (2023C03155), National Natural Science Foundation of China (72171210), Zhejiang Provincial Natural Science Foundation of China (LZ23E080002), and the Smart Urban Future (SURF) Laboratory, Zhejiang Province.  
Not applicable.  
\\begin{table}
\\begin{tabular}{c c c c} \\hline \\hline
**Performance** & **DDSM** & **SP** & **TDSP** \\\\ \\hline LCR & 84.78\\% (\\(\\pm\\)0.444\\%) & 77.98\\% (\\(\\pm\\)0.599\\%) & 81.96\\% (\\(\\pm\\)0.489\\%) \\\\ PLCR & 85.02\\% (\\(\\pm\\)0.449\\%) & 79.19\\% (\\(\\pm\\)0.683\\%) & 82.92\\% (\\(\\pm\\)0.455\\%) \\\\ AVG & 84.90\\% (\\(\\pm\\)0.438\\%) & 78.59\\% (\\(\\pm\\)0.648\\%) & 82.44\\% (\\(\\pm\\)0.465\\%) \\\\ \\hline \\hline \\end{tabular}
\\end{table}
Table 6: Effectiveness of different path completion algorithms after weighting across ten testing sets.