## 2 Concept Definition and Calculation
### Calculation of the Eigenvalues of Influencing Factors  
(1) Calculation of characteristic values  
In this paper, Foshan Bus No. 101, whose operation is located in the urban area and the quality of data collection is relatively good, was selected as an example to calculate the station delay, cross delay and road delay of Bus No. 101 in the upward direction from 21 November to 25 November 2016, respectively. The total length of Foshan Bus 101 is 12.86 km, via 27 stations and 19 signal intersections. Python software was used to calculate the delays for all trips issued on Foshan Bus 101 during the five days from 21 November to 25 November 2016. The trips were numbered in order from north to south. Taking one trip as an example, the statistical tables are shown in Tables 2-4. The valid data of all 431 trips in a 5-day working day is counted by circular calculation. The weather factor and driver driving behavior factor do not need to be calculated and are assigned separately according to daily weather and license plate number. (2) Feature correlation analysis  
The SPSS software was used to conduct correlation analysis between different factors and bus operation health class, and the influencing factors with higher correlation were screened as the feature set of the random forest algorithm. Correlation analysis was conducted for total station delay, total cross delay, total road delay, daily weather, driving behavior, and bus health class for each trip. The correlation coefficient analysis mainly calculates the Pearson correlation coefficient and significance coefficient of the two data sets, which are calculated as follows. (1) Pearson correlation coefficient  
\[r=\frac{\sum(x-\overline{x})(y-\overline{y})}{\sqrt{\sum(x-\overline{x})^{2} \sum(y-\overline{y})^{2}}} \tag{4}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **Weather** & **Assignment** \\ \hline
21-11-2016 & Cloudy & 2 \\
22-11-2016 & Light rain & 4 \\
23-11-2016 & Medium Rain & 4 \\
24-11-2016 & Light rain & 3 \\
25-11-2016 & Sunny & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weather conditions. In Formula (4), \(x\) and \(y\) represent sample values, and \(x\) and \(\overline{y}\) represent sample means. The closer the \(r\) value is to 1, the stronger the correlation is. (2) Significance coefficients are expressed by the T-statistic  
\[\text{T}=\frac{r\sqrt{n-2}}{\sqrt{1-r}} \tag{5}\]  
In Formula (5), \(r\) denotes the correlation coefficient and \(n\) denotes the sample size. \begin{table}
\begin{tabular}{c c c c} \hline
**Section No.** & **Road Delay** & **Section No.** & **Road Delay** \\ \hline road\_1 & 38.966 & road\_11 & 922.869 \\ road\_2 & 272.049 & road\_12 & 378.659 \\ road\_3 & 660.432 & road\_13 & 232.953 \\ road\_4 & 313.809 & road\_14 & 91.732 \\ road\_5 & 948.306 & road\_15 & 716.192 \\ road\_3 & 660.432 & road\_13 & 232.953 \\ road\_6 & 17.342 & road\_16 & 299.874 \\ road\_7 & 557.481 & road\_17 & 28.525 \\ road\_8 & 102.213 & road\_18 & 966.225 \\ road\_9 & 60.817 & road\_19 & 49.424 \\ road\_10 & 807.132 & Total & 7396.55 \\ \hline \end{tabular}
\end{table} 
Table 4: Road delay statistics.