## 5 Experiments
### Results and Analysis
#### 5.3.2 Travel Time Interval Prediction Results
During the off-peak time, as shown in Figure 6b, as the fluctuation range of the true value decreases, the predicted interval width of \(\rm PI_{MHC}\) is significantly narrower compared to the other two periods. This indicates that \(\rm PI_{MHC}\) prediction performs better during periods of stable traffic flow. However, when there is a drastic fluctuation in the actual value, as shown in Figure 6c during the evening peak period, the prediction error is relatively large and the true value falls outside the range. We believe that the traffic situation at this time has been affected by an unexpected event. Figure 7a-c show the interval prediction results of driver 902359 during morning peak hours, off-peak hours, and evening peak hours, respectively. It can also be seen from the results that the prediction results of the three models \(\rm PI_{MHC}\), \(\rm PI_{HC}\), and \(\rm PI_{P}\) can all include most of the true values within the prediction interval. At the same time, the width of \(\rm PI_{MHC}\) is significantly narrower than \(\rm PI_{HC}\) and \(\rm PI_{P}\) in the prediction results based on the sharing of multiple route segments in three time periods. And the predicted values can track the changes in the measured values well. However, there was no significant fluctuation in the actual value and almost all true values were within the range. Next, we will quantitatively evaluate the generated PI and further analyze the effectiveness of interval prediction. By calculating the PICP, MPIW, NMPIW, and CWC, the accuracy of prediction results for each interval can be accurately compared. Table 4 shows the values of PICP, MPIW, NMPIW and CWC for the prediction interval when the confidence level is 90%. It can be seen that almost all predicted PICP values for each PI of \(\rm PI_{MHC}\) exceed the set confidence level (90%). In all cases, the MPIW value of \(\rm PI_{MHC}\) is smaller than the corresponding MPIW values of \(\rm PI_{HC}\) and \(\rm PI_{P}\). For example, in case 902335, the MPIW values in three time periods decreased by 101.04 s, 151.72 s, and 33.87 s in \(\rm PI_{MHC}\) compared to \(\rm PI_{HC}\), respectively. Compared to \(\rm PI_{P}\), it decreased by 67.25 s, 180.29 s, and 96.79 s, respectively, indicating that the predicted interval width significantly narrowed without reducing the PICP. The values of the corresponding NMPIW also decreased, with \(\rm PI_{MHC}\) decreasing by 18.44, 17.43, and 11.08 compared to \(\rm PI_{HC}\), respectively. Compared to \(\rm PI_{P}\), it decreased by 27.5, 38.87, and 28.69, respectively. Meanwhile, the corresponding CWC values were also smaller (decreased by 102.09, 17.43, and 1210.65, respectively). Similarly, in Table 4, it can be observed that the predicted results of case 902359 show a similar trend over three time periods. The value of MPIW was decreased by 126.58 s, 127.47 s, and 17.06 s in \(\rm PI_{MHC}\) compared to \(\rm PI_{HC}\), respectively. Compared to \(\rm PI_{P}\), it decreased by 147.16 s, 158.4 s, and 51.27 s, respectively. The corresponding NMPIW values also decreased, with \(\rm PI_{MHC}\) decreasing by 25.42, 21.66, and 12.82 compared to \(\rm PI_{HC}\), respectively. Compared to \(\rm PI_{P}\), it decreased by 31.53, 31.65, and 25.41, respectively. Meanwhile, the corresponding CWC value is also smaller. Furthermore, it indicates that the interval prediction quality of the method proposed in this article is better, which can shorten the waiting time of passengers and provide more reliable waiting time recommendations.