## 5 Experiments
### Results and Analysis
#### 5.3.2 Travel Time Interval Prediction Results  
According to the driving style clustering results in Section 5.3.1, two drivers numbered 902335 and 902359 are selected as the travel time PI prediction objects in this section. The departure schedule of the two drivers to be predicted from 21 January 2016 to 22 January 2016 is shown in Table 3.  
The experiment used the MATLAB R2016a software package to build PIs running on a Core I7-4790, 3.6-GHZ CPU with 8-GB RAM. Each experiment was repeated 20 times, and the average value was calculated as the final result. Meanwhile, to obtain high-confidence prediction information, we set the confidence level to 90%. The predicated PI results are shown in Figures 6 and 7, respectively. To compare the effectiveness of the proposed method, we compared it with the reference results [8], which used a data set of Route 239 to construct personalized travel time prediction intervals and prediction intervals after driving style clustering, respectively. For simplicity, we use P, HC, and MHC subscripts to represent the PI results generated from a single driver data set, a single bus route hierarchical clustering data set, and a multiple bus routes hierarchical clustering data set, respectively. For example, taking 902335 as an example, PI\({}_{\text{IP}}\) represents the PI result generated using only the data set of drivers with ID 902335. PI\({}_{\text{HC}}\) represents the PI result generated by the hierarchical clustering of driving styles using all drivers on a single route (239). PI\({}_{\text{MHC}}\) represents the PI result generated by hierarchical clustering of all drivers on multiple routes according to their driving styles. The results of the travel time PIs of driver 902335 are shown in Figure 6. The prediction results are divided into three subgraphs according to time periods, where subgraphs a, b, and c, respectively, show the PIs results of driver 902335 during morning peak hours, off-peak hours, and evening peak hours. Figure 5: The clustering results during the evening peak time (16:00–19:00) of the three road segments. (**a**) Result of road segment 1, (**b**) result of road segment 2, (**c**) result of road segment 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time Period**} & \multicolumn{2}{c}{**21 January 2016**} & \multicolumn{2}{c}{**22 January 2016**} \\ \cline{2-5}  & **902335** & **902359** & **902335** & **902359** \\ \hline
7–9 & 7:23:07 & 7:13:38 & 8:58:02 & 7:05:45 \\ \hline
9–16 & 10:06:04 & 9:57:11 & 11:55:28 & 9:37:12 \\  & 14:19:48 & 12:57:05 & 12:45:11 & 12:45:11 \\  & 15:14:05 & 14:38:04 & 15:58:15 \\ \hline
16–19 & 16:42:22 & 17:36:00 & 17:00:06 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The predicted departure schedule of the drivers. Figure 6: PI results of 902335. (**a**) The morning peak time (7:00–9:00), (**b**) the off-peak time (9:00–16:00), (**c**) the evening peak time (16:00–19:00). It is not difficult to see from the results that the prediction results of the three models PI\({}_{\text{MHC}}\), PI\({}_{\text{HC}}\), and PI\({}_{\text{P}}\) can all include most of the true values within the prediction interval. Meanwhile, the width of PI\({}_{\text{MHC}}\) based on multiple routes sharing prediction results is significantly narrower than that of PI\({}_{\text{HC}}\) and PI\({}_{\text{P}}\) in the three time periods. And the  
Figure 7: PI results of 902359. (**a**) The morning peak time (7:00–9:00), (**b**) the off-peak time (9:00–16:00), (**c**) the evening peak time (16:00–19:00). predicted values can track the changes in the measured values well. During the morning peak time, as shown in Figure 6a, only the predicted interval of \(\rm PI_{MHC}\) fully includes the true values, and the predicted interval width is the narrowest. This indicates that the prediction interval of \(\rm PI_{MHC}\) is significantly better than the other two models.