# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 4 Case Study
### Discussion  
In this study, a new method combining the hierarchical clustering algorithm and support vector regression model was proposed to study the influence of driving styles on the prediction of travel time. In previous studies on the prediction of bus travel times, other scholars have considered the impact of changes in passenger flow [39], traffic conditions [40; 41], space-time factors [42; 43; 44], signals [45; 46; 47], weather [40], and other factors on the prediction of travel time. Compared to these influencing factors, it is more difficult to obtain driving style data. Therefore, to our knowledge, there has not been research that has taken into account driving styles in the prediction of bus travel times. In addition, although there are a lot of traffic data being collected currently, they lack accurate processing. How to extract feature sets that can accurately predict bus travel times from these data is still worth studying [45]. Recently, some meaningful research has been conducted on this subject. For example, buses' running times for multiple routes were used to predict arrival times for each route, which improves prediction precision [30]. [PERSON] et al. [44] uses clustering based on the homogeneity of travel time observations and potential traffic conditions to estimate the probability distribution of travel time according to the travel time distribution of road sections. [PERSON] et al. [40] explored the common travel time patterns of different bus line sections and divided bus line sections with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Driver** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline
902334 & 94.5 & 64.8 & 59.7 & 63.8 \\ \hline
902335 & 58.6 & 47.1 & 55.3 & 65.0 \\ \hline
902351 & 72.5 & & & 58.0 \\ \hline
902353 & 92.1 & 71.3 & 82.0 & 80.1 \\ \hline
902355 & 59.5 & 45.1 & 48.0 & 61.0 \\ \hline
902359 & 49.3 & 39.8 & 53.6 & 53.3 \\ \hline _g-RMSE_ & 71.1 & 53.6 & 59.7 & 63.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction results of four models (_RMSE_).  
Figure 9: The influence of different clustering results on model prediction performance (_RMSE_).  
similar patterns into the same cluster. Then these clusters were merged to extract data records for model training and the prediction of bus travel times. This paper also considers driving styles from the perspective of optimizing the prediction dataset. For the first time, driving styles were used to optimize the training and prediction dataset, and then the optimized datasets were used to predict the bus travel times. The effectiveness of the proposed method is verified by experiments.  
Moreover, from our experimental results, we also found that the _MAE_ values of the four models were higher than those of other periods in the early peak hours. One of the possible reasons for this is that the route selected for research in this paper is one-way, driving from the outside of the city to the center of the city. The number of passengers and vehicles entering the city in the early hours of the morning is greater than in other periods, leading to more uncertain factors in terms of travel time. We believe that the impact of traffic conditions and drivers' driving styles on bus travel times is both relevant and restrictive. Subsequent research should consider the comprehensive impact of multiple factors, which can further improve the accuracy of predictions. Although the prediction quality is low during peak hours, our framework can also significantly improve prediction accuracy.