# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 6 Conclusions  
A high-efficiency mobile positioning method is proposed to collect and analyze the cellular network signals of CVO data. Parallel computing and cloud computing techniques are designed into the proposed mobile positioning method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with different outlier filtering criteria. In experimental environments, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom and to analyze the cellular network signals of CVO data for location determination. A case study determined that the average location errors using the proposed method and the traditional cell-ID-based method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS and the cell-ID-based method for each road type, particularly urban roads. Therefore, this approach is feasible to determine the location of an OBU for CVO improvement.  
In future work, the signals of neighboring cells can be analyzed simultaneously to improve mobile positioning. Moreover, the proposed method for generating precise location information can be applied to support other ITS applications (e.g., advanced public transportation services, advanced traffic information services, _etc._).  
**Acknowledgments: We would like to thank editor and reviewers for their comments. We also thank MDPI publisher for their supports.**  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Type** & **Cell ID Based Method** & **The Proposed Method** \\ \hline National Highway & 858.3 & 249.6 \\ Provincial Highway & 444.4 & 133.6 \\ Urban Road & 458.4 & 133.2 \\ County Road & 918.7 & 53.8 \\ Village Road & 324.6 & 49.3 \\ Alley & 474.8 & 132.9 \\ Average & 705.0 & 176.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The analyses of average location errors for each road type (unit: m).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Type** & **The Number of Records** & **Ratio** \\ \hline National Highway & 2,396,671 & 36.470\% \\ Provincial Highway & 559,433 & 8.513\% \\ Urban Road & 2,802,047 & 42.639\% \\ County Road & 275,894 & 4.198\% \\ Village Road & 237,131 & 3.608\% \\ Alley & 300,374 & 4.571\% \\ Total & 6,571,550 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ratio of historical records for each road type.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Type** & **GPS** & **Cell ID Based Method** & **The Proposed Method** \\ \hline National Highway & 99.996\% & 99.998\% & 99.998\% \\ Provincial Highway & 99.997\% & 99.999\% & 99.999\% \\ Urban Road & 95.296\% & 99.951\% & 99.943\% \\ County Road & 99.990\% & 99.996\% & 99.996\% \\ Village Road & 99.994\% & 99.999\% & 99.999\% \\ Alley & 99.977\% & 99.994\% & 99.994\% \\ Average & 99.208\% & 99.989\% & 99.988\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The analyses of availabilities for each road type.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.