## 2 Concept Definition and Calculation
### Calculation of the Eigenvalues of Influencing Factors
\begin{table}
\begin{tabular}{c c c c c} \hline
**Station Order** & **Site Name** & **Station Delay** & **Station Order** & **Site Name** & **Station Delay** \\ \hline
0 & Train Station & - & 14 & Yuejin Road Middle & 19 \\
1 & Bus Station A & 13 & 15 & Meishu Ceramic Factory & 5 \\
2 & Chinese Hospital & 20 & 16 & Hualong Palace & 6 \\
3 & Cyclone Hotel & 23 & 17 & Sha Gang & 24 \\
4 & Zumiao A Station & 8 & 18 & Tamecheong-ri & 3 \\
5 & Global International Plaza & 35 & 19 & Home Expo City South Gate & 8 \\
6 & Customs & 15 & 20 & Lanshi Bridge West & 10 \\
7 & Foshan Institute of & 0 & 21 & Foshan Home Expo City & 8 \\
8 & Pil Tang & 4 & 22 & Lanshi Road East & 5 \\
9 & Tea Pavilion & 4 & 23 & Yin Yuan Market & 10 \\
10 & Shiwan Bus Station & 16 & 24 & Lanshi High School & 7 \\
11 & China Ceramic City & 3 & 25 & Provincial Spinning Institute & 5 \\
12 & Shiwan Park & 24 & 26 & Stone Village & - \\
13 & Tao Du & 8 & & Total & 283 \\ \hline \end{tabular}
\end{table} 
Table 2: Station delay statistics. \begin{table}
\begin{tabular}{c c c c} \hline
**Signal No.** & **Cross Delay** & **Signal No.** & **Cross Delay** \\ \hline ID\_1 & 23.696 & ID\_11 & 13.004 \\ ID\_2 & 41.4 & ID\_12 & 44.2 \\ ID\_3 & 33.85 & ID\_13 & 58.93 \\ ID\_4 & 10.43 & ID\_14 & 48.187 \\ ID\_5 & 13.727 & ID\_15 & 17.404 \\ ID\_6 & 25.2 & ID\_16 & 37.617 \\ ID\_7 & 15.986 & ID\_17 & 12.711 \\ ID\_8 & 61.4 & ID\_18 & 24.788 \\ ID\_9 & 31.416 & ID\_19 & 54.329 \\ ID\_10 & 21.4 & Total & 589.675 \\ \hline \end{tabular}
\end{table} 
Table 3: Cross delay statistics. The statistical results are shown in Table 5. The absolute values of Pearson correlation for both weather and driver are less than 0.6 and the significance coefficients are greater than 0.01. It indicates that the weather factor and driver behavior factor are not significantly correlated with the health grade of bus operation. The Pearson correlation coefficients for total road delay, total cross delay, and total station delay are greater than 0.6 with a significance coefficient of 0. It indicates that the delay factor is significantly correlated with the bus operation health rating at 99% confidence interval. The total delay box line diagram for bus operation under different weather conditions is shown in Figure 5. The box line plot shows that the median, upper quartile, and lower quartile delays do not differ significantly by date. This indicates that weather changes do not have a significant impact on the operation of the 101 bus vehicles. Similarly, the average values of delays of different license plates at the same time period within 5 days are counted, as shown in Figure 6. The average values of delays exhibited by different license plates at the same time period have a uniform pattern, with a significant increase in delay values during peak periods. It shows that driver behavior also has little impact on the health of public transport operations, which is consistent with the conclusions of the correlation analysis. Therefore, this paper focuses on constructing feature sets with delay factors and constructing a random forest algorithm to study the key factors affecting the health of bus operation. Merging the three types of features, the final features of each bus trip are represented by a 63-dimensional vector, as shown in Figure 7.  
Based on this, a decision tree classification algorithm is used to judge the health classification for each trip as the data label, and the above 63 \(\times\) 431 dimensional matrix is used as the feature set to construct the data set, and a random forest algorithm is used to construct a model to explore the degree of influence of different influencing factors on Foshan Bus Route 101. Figure 5: Box plot of delay statistics for different weather conditions.