# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 4 Case Study
### Results  
In order to select the best dataset for the prediction of travel time, four different levels of datasets are selected from the clustering results in the previous section of the experiment, which are divided into nine categories, five categories, four categories, and one category. For example, the training datasets corresponding to the four prediction models of driver number 902334 are shown in Table 2. Among them, Model 1 is when the dataset is clustered into nine categories, that is, the travel time history data of a single driver are used for training and prediction. Model 4 is trained and predicted by the travel time history data of all drivers when the dataset is clustered into one category. Model 2 and Model 3 use the historical data of 3-6 drivers' travel time for training and prediction when they are clustered into five and four categories, respectively, according to the clustering results. The four models were experimentally evaluated, and six drivers were selected as experimental subjects on 18 January 2016. Table 3 lists the scheduled departure schedules of the six predicted drivers on 18 January 2016. The travel time of each driver will be predicted using four models from different datasets. Figure 6 shows the _MAE_ values of the predicted results of all the trips of the buses driven by the six drivers under the four prediction models. Table 4 is the average _MAE_ of all the trips of each bus in one day under the four prediction models that are used for the forecast. The global _MAE_ (_g-MAE_) score measures the average _MAE_ of all the buses driven by the six drivers in the four models. The bold results are the results with the best performance for this experiment. (The bold results in the following tables have the same meaning.) The results show that the performance of Model 2 and Model 3 is better than that of Model 1 using only a single driver history dataset and Model 4 using all the datasets, and the prediction error of Model 2 is the smallest (compared to model 1, the accuracy is improved by 24.5%, compared to Model 3, the accuracy is improved by 9.1%, and compared to Model 4, the accuracy is improved by 13.4%). Obviously, the prediction effect of Model 1 using only the historical data of a single driver is the most unsatisfactory, which may be the result of too few training data. However, the accuracy of the prediction results is not as simple as the more data, the better. The prediction effect of Model 4 using all the data is not the best. We think that the main reason for this is that the driving behaviors of the drivers are different, resulting in a wide distributi \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Number of Clusters** & **7:00-9:00** & **9:00–16:00** & **16:00–19:00** \\ \hline Model 1 & 9 & 902334 & 902334 & 902334 \\ \hline Model 2 & 5 & 902334, 902335, 902349, 902355 & 902334, 902349, 902335 & 902334, 902349, 902359 \\ \hline Model 3 & 4 & 902334, 902335, 902349, 902359 & 902334, 902335, 902349, 902335, 902349, 902359, 902359 & 902334, 902335, 902359, 902359 \\ \hline Model 4 & 1 & All data & All data & All data \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The training datasets corresponding to the four prediction models of driver number 902334. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time Period** & **902334** & **902335** & **902351** & **902353** & **902355** & **902359** \\ \hline
7–9 & 8:27:35 & & 7:37:39 & 7:16:42 & & 7:21:04 \\ \hline
9–12 & 11:39:54 & 9:36:14 & 11:26:30 & 10:25:45 & 9:47:19 & 10:14:51 \\ \hline
12–16 & 13:49:38 & 12:27:07 & 13:54:07 & 14:19:43 & 12:39:59 & 15:13:35 \\ \hline
16–19 & 16:25:51 & & 16:40:31 & 17:05:54 & & 17:45:58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The schedule of the six predicted drivers on 18 January 2016. Figure 6: The _MAE_ values of the predicted results of all trips of the buses driven by six drivers under the four prediction models. (**a**) 902334. (**b**) 902335. (**c**) 902351. (**d**) 902353. (**e**) 902355. (**f**) 902359.