## 4 Case Study
### Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**The Backpropagation-Based**} & \multicolumn{2}{c}{**Optimal Strategy**} \\
**Region** & **HSR Station** & \begin{tabular}{c} **Observed** \\ **Volume** \\ **(Persons/Day)** \\ \end{tabular} & \begin{tabular}{c} **Estimated** \\ **Volume** \\ **(Persons/Day)** \\ \end{tabular} & \begin{tabular}{c} **Error Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Estimated** \\ **Volume** \\ **(Persons/Day)** \\ \end{tabular} & \begin{tabular}{c} **Error Rate** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Estimated** \\ **Volume** \\ **(Persons/Day)** \\ \end{tabular} &
\begin{tabular}{c} **Error Rate** \\ **(\%)** \\ \end{tabular} \\ \hline  & Seoul & 37,867 & 38,907 & 0.6 & 21,103 & \(-\)44.3 \\  & Suseo & 19,509 & 19,742 & 1.2 & 22,743 & 16.6 \\  & Yongsan & 13,488 & 13,720 & 1.7 & 15,092 & 11.9 \\  & Gwangmyeong & 12,867 & 13,096 & 1.8 & 12,709 & \(-\)1.2 \\  & Dontan & 3682 & 3916 & 6.4 & 4933 & 34.0 \\  & Cheongyangni & 2617 & 2847 & 8.8 & 8478 & 224.0 \\  & Hangsin & 2111 & 2239 & 6.1 & 5567 & 163.7 \\  & Suwon & 1598 & 1735 & 8.6 & 1100 & \(-\)31.2 \\  & Jie & 1546 & 1411 & \(-\)8.7 & 1428 & \(-\)7.6 \\ \cline{2-6}  & MAE (MAPE) & - & 153 & 3.5 & 3310 & 53.6 \\ \hline  & Busan & 24,062 & 24,066 & 0.0 & 21,690 & \(-\)9.9 \\  & Dongdaegu & 23,564 & 23,246 & \(-\)1.3 & 22,856 & \(-\)3.0 \\  & Daejeon & 17,634 & 17,500 & \(-\)0.8 & 19,025 & 7.9 \\  & Cheonan- & 11,568 & 11,430 & \(-\)1.2 & 11,283 & \(-\)2.5 \\  & Osong & 10,061 & 9925 & \(-\)1.4 & 10,218 & 1.6 \\  & Gwangju- & 9474 & 9496 & 0.2 & 9184 & \(-\)3.1 \\  & Songjeong & 6770 & 6776 & 0.1 & 8073 & 19.2 \\  & Ulsan & 5461 & 5403 & \(-\)1.1 & 4704 & \(-\)13.9 \\  & Gangneung & 4478 & 3922 & \(-\)12.4 & 3914 & \(-\)12.6 \\  & Singyeongju & 4396 & 4401 & 0.1 & 2541 & \(-\)42.2 \\  & Gimcheon- & 3182 & 3284 & 3.2 & 3463 & 8.8 \\  & Gumi & 3057 & 3057 & - & 3050 & \(-\)0.2 \\  & Pohang & 3057 & 3057 & - & 3050 & \(-\)0.2 \\ \hline MAE (MAPE) & - & 123 & 1.8 & 831 & 10.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Trip assignment results for the major HSR stations. unbalanced, resulting in a large error rate. For example, in the Seoul metropolitan area, an error rate of more than 40% was found for Seoul, Cheongnyangni, and Haengsin Stations. Furthermore, in the non-metropolitan area, the demand that should have been assigned to Singyeongju Station was assigned to Ulsan Station, so the error rates were 19.2% and \(-\)42.2%, respectively, because the optimal strategy algorithm based on the all-or-nothing trip assignment was able to simulate the diverse access trip patterns of passengers in areas with multiple HSR stations, such as the Seoul metropolitan area. Therefore, after the initial assignment from the optimal strategy algorithm, it was necessary to reduce the error rate within the reference value using a parameter calibration process by adjusting the network parameters. The analyst's arbitrary judgment was involved in this process, and much time and effort were required. For stations in the Seoul metropolitan area, the MAE and MAPE indicators were 153 and 3.5%, respectively, when using the backpropagation-based algorithm, and those when using the optimal strategy algorithm were 3310 and 53.6%, respectively. Moreover, for the stations in the non-metropolitan area, the MAE and MAPE values were 123 and 1.8%, respectively, when using the backpropagation-based algorithm, and those when using the optimal strategy algorithm were 831 and 10.0%, respectively. Therefore, the estimation accuracy of the backpropagation-based algorithm developed in this study was superior to that of the optimal strategy algorithm.