# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 4 Results and Discussion
### Contrast Experiments
The performance of PCF-unweight on the four routes is inferior to that of PMLNet, which demonstrates that the real-time traffic factors are effective in reducing the deviation of bus travel time prediction. The overall performance of PMLNet is also optimal as shown in Table 11. Compared with other methods, both the MAE and MAPE of PMLNet reach a small value. The MAPE is 2.91%, which indicates the effectiveness of the PMLNet model. The MAE is 1.45 min, which indicates that the average difference between the estimated travel time and the actual travel time on the four travel routes is 1.45 min, which is a low value acceptable to the passengers. The experimental results show that the \(\alpha\) correction factor improves the prediction accuracy of each route by about 15-40% by integrating real-time traffic flow information. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Route1**} & \multicolumn{2}{c}{**Route2**} & \multicolumn{2}{c}{**Route3**} & \multicolumn{2}{c}{**Route4**} \\ \cline{2-9}  & **MAE** & **MAPE** & **MAE** & **MAPE** & **MAE** & **MAPE** & **MAE** & **MAPE** \\  & **(min)** & **(\%)** & **(min)** & **(\%)** & **(min)** & **(\%)** & **(min)** & **(\%)** & **(min)** & **(\%)** \\ \hline HA [23] & 5.63 & 11.28 & 7.12 & 9.41 & 6.35 & 13.57 & 4.78 & 15.77 \\ SVR [29] & 4.43 & 11.25 & 6.75 & 8.33 & 5.93 & 10.49 & 3.48 & 14.25 \\ Pure-MDARN & 4.12 & 7.03 & 4.99 & 6.33 & 4.70 & 7.19 & 4.54 & 8.35 \\ PCF-LR [9] & 3.01 & 6.18 & 3.91 & 4.52 & 3.43 & 5.76 & 2.75 & 8.27 \\ PCF-unweight & 2.46 & 4.79 & 3.89 & 3.96 & 3.64 & 4.84 & 2.29 & 5.82 \\ PCF-LSTM & 2.25 & 4.85 & 3.40 & 3.35 & 3.49 & 4.95 & 2.18 & 5.72 \\ PCF-DARNN & 2.16 & 4.14 & 2.94 & 3.11 & 2.65 & 3.05 & 1.86 & 4.62 \\ PMLNet & 1.89 & 2.69 & 2.48 & 1.86 & 2.07 & 2.06 & 1.58 & 3.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of different methods on multiple routes. The client firstly collects and processes the real-time macro and local factors, and then sends the real-time location information to the client page, while the real-time data is sent to PMLNet and the server, respectively. After obtaining the features, the PMLNet module outputs the predicted bus travel time. Finally, the client page displays the real-time location information and predicted bus travel time. On the server side, the real-time data, actual travel time, and predicted travel time are stored in the database. As sufficient data accumulates, the model can undergo incremental training on the server to enhance its generalization capability. The system was tested on the XIAOMI 10 in 15 June 2023, and the test routes were route 805 to 202, where the start is Jialing Garden, the end is Golden Fort, and the tester would transfer at Shapingba Station. The client screenshots show the user interface of the bus travel time prediction system. This interface is designed to provide users with key information about their bus trips. The main elements displayed include the route map, predicted and actual travel times, and important station markers. Before starting their trip, users can interact with the interface by checking the predicted times to plan their commute. After the trip, they can compare the actual travel times with the predicted ones to assess the system's accuracy. Figure 14 shows the client page at the start location and the destination. In (a), the predicted travel time consists of two driving periods and one waiting period, while (b) demonstrates the actual travel time of three periods. The test results reveal that the error between the predicted travel time and the actual travel time is less than two minutes, which proves that the bus travel time prediction system can accurately identify the real-time location of the bus and predict the travel time with high accuracy.