## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Origin** & **Destination** & **Travel Time (s)** & **Intervallic Travel Time (s)** & **Period Number** & **Time Estimated Using Electronic Maps (s)** & **Sample Size** & **Time Difference** & **Reference Ratio** \\ \hline Daxi & Zhonghe & 3415 & 3270 & 7 & 2796 & 17 & 17.0\% & 100.3\% \\ Warehouse & Shiji Store & 3158 & 3520 & 18 & 2796 & 26 & 25.9\% & 100.4\% \\ Zhonghe & Daxi & 3158 & 3520 & 18 & 2796 & 26 & 25.9\% & 100.4\% \\ Store & Warehouse & & & & & & & \\ Emei & Nanzhuang & & & & & & \\ Taismaxian & Guihuanyuan & 1690 & 1600 & 8 & 1608 & 271 & 0.5\% & 54471.0\% \\ Store & Store & & & & & & & \\ Taichung & Taichung & 1588 & 1682 & 23 & 2796 & 40 & 39.8\% & 100.4\% \\ Fuda Store & Warehouse & & & & & & & \\ Yangmei & Yangmei & 563 & 578 & 13 & 386 & 50 & 49.6\% & 100.7\% \\ Station Store & Beidu Store & & & & & & & \\ Zhongli & Zhongli & 427 & 487 & 12 & 566 & 14 & 14.0\% & 100.3\% \\ Yuanxing & Longgang & 427 & 487 & 12 & 566 & 14 & 14.0\% & 100.3\% \\ Store & Store & & & & & & & \\ Meihua Store & & 295 & 286 & 9 & 185 & 55 & 54.9\% & 100.2\% \\ Tainan & Taian & & & & & & & \\ Changci & Beizhou & 280 & 343 & 16 & 258 & 33 & 32.9\% & 100.2\% \\ Store & Store & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Some calculated reference ratio values (Time unit: seconds). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Records** & **Number of Original GPS** & **Number of Travel Time** & **Networks** & **Number of Average Time** & **Number of Records with a Sample Size** & **Number of Reference Ratio** \\
**Tracks** & **Process** & **Records** & **Removing** & **Records** & **Higher than the** & **Higher than the** & **Threshold** \\  & & & & **Outliers** & **Threshold** & **Threshold** \\ \hline Intervallic records & 65,249,808 & 3,794,744 & 3,779,725 & 199,872 & 75,289 & 60,344 \\ Daily records & 65,249,808 & 3,794,744 & 3,728,516 & 77,765 & 21,330 & 17,066 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Variations in the number of records in the data processing stages (Time unit: seconds). times. All of this study's daily travel times fell within the corresponding estimated travel time ranges for next-day departure on Google Maps. An intervallic combination with a small sample of travel records hinders accurate travel time estimation by dispatchers in the scheduling of future delivery along a certain route in a specific period. Therefore, in this study, the intervallic travel time records were employed in machine learning to generate estimated travel times for other periods. Consider the route from Guanshan Minzu Store to Taitung Luye Store (Table 8) as an example. Table 9 reveals the hourly travel times for this route and indicates that the average travel time data were available only for periods 2-6 and periods 9-12 but not for other periods. These available data were used to construct a polynomial regression equation: \(y=1.6801x^{3}-39.831x^{2}+263.53x+1083.5\), where \(x\) denotes the period, and \(y\) is the travel time (Figure 9). If a dispatcher must schedule delivery vehicles to travel from Guanshan Minzu Store to Taitung Luye Store in a period for which average travel time data are unavailable, we can use the aforementioned equation to estimate the travel time. In Table 9, there is no average travel time between period 7 and period 8. After applying the regression equation, the predicted travel time in period 7 is 1552.76 s, and the relevant calculation is presented in Equation (5). The predicted travel time in period 8 is 1502.76 s, and the relevant calculation is presented in Equation (6). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline