## 6 Conclusions  
Travel time is an important indicator in intelligent transportation systems, and currently, most research on travel time focuses on deterministic prediction, requiring decision-makers to evaluate the reliability of results through the error value of the model. This article conducts in-depth research on the uncertainty of travel time prediction. To further improve the quality of bus travel time PIs, this paper outlines a multi-faceted approach comprising segmentation of routes, clustering of drivers based on driving styles, and bootstrap method application. We propose a bus travel time PIs model based on road sharing and driving style similarity for multiple routes. The superiority of the proposed method was demonstrated through experiments on real data from multiple bus routes in Shenyang city. We found that the width of PIs based on multiple routes' segment sharing significantly narrows. In the two cases studied, the MPIW for the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively. And the predicted values can better track the changes in the measured values. The findings suggest potential implications for improving public transportation systems, emphasizing the importance of incorporating shared data and driving behavior analysis for more accurate predictions. The results of this study can serve as a reference for public transportation management agencies to arrange the operation of public transportation vehicles more scientifically and reasonably and also help travelers better plan their itinerary and reduce their waiting time.  
Future research can be conducted from the following two aspects: (1) combining deep learning models to explore the impact of deep-level spatiotemporal factors on travel time prediction intervals; (2) developing a component for the prediction of traffic accidents to enhance the predictive ability of existing models in dealing with unexpected situations.  
Conceptualization, B.Z. and Z.Y.; methodology, Z.Y.; software, B.W.; validation, Z.Y. and B.W.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y.; supervision, X.S.; project administration, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of the National Natural Science Foundation of China (U1908212), the Central Government-Guided Local Science and Technology Development Fund Project (1653137155953), and Liaoning Province \"Takes the Lead\" Science and Technology Research Project (2021 jh1/10400006).  
The data presented in this study are available on request from the corresponding author.  
The authors wish to express their gratitude to Shenyang Municipal Bureau of Big Data for providing the experimental data set.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Case Study** & **Model** & **PICP (\%)** & **MPIW** & **NMPIW (\%)** & **CWC** \\ \hline \multirow{8}{*}{902359} & \multirow{2}{*}{7:00–9:00} & PI\({}_{\text{IP}}\) & 94.44 & 395.85 & 64.51 & 64.51 \\  & & PH\({}_{\text{HC}}\) & 100.00 & 375.27 & 58.4 & 58.4 \\  & & PH\({}_{\text{MHC}}\) & 100.00 & **248.69** & **32.98** & **32.98** \\ \cline{1-1}  & & PI\({}_{\text{IP}}\) & 100.00 & 336.43 & 57.31 & 57.31 \\ \cline{1-1}  & & PI\({}_{\text{HC}}\) & 100.00 & 305.5 & 47.32 & 47.32 \\ \cline{1-1}  & & PI\({}_{\text{MIC}}\) & 96.3 & **178.03** & **25.66** & **25.66** \\ \cline{1-1} \cline{2-6}  & & PI\({}_{\text{IP}}\) & 100.00 & 281.1 & 67.91 & 67.91 \\ \cline{1-1}  & & PI\({}_{\text{HC}}\) & 100.00 & 246.89 & 55.32 & 55.32 \\ \cline{1-1}  & & PI\({}_{\text{MIC}}\) & 100.00 & **229.83** & **42.5** & **42.5** \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._