## 5 Discussion and Conclusions  
In this paper, the bus operation was divided into two parts, including bus traveling between bus stops and bus dwelling at fixed stops, to analyze the process of bus bunching formation. Delays in bus operation make bus headway at stops deviate from the schedule and finally cause bus bunching. The occurrence of bus bunching is accompanied by the phenomenon of \"Large and small intervals\", which is worsened during peak times compared to off-peak time. After analyzing the headway and headway stability temporally and spatially, it could be concluded as follows:  
1. One single day. A small headway usually occurred from 7:00 to 10:00 and 17:00 to 19:00, while a large headway frequently occurred from 13:00 to 15:00 and 21:00 to 24:00. The _Cov_ generally had a local peak from 7:00 to 9:00, 17:00 to 20:00, and even at 21:00 after the regular evening peak. 2. Different time types. The headway during the morning peak on weekdays was the most unstable, followed by the evening peak on weekdays. The most stable headway usually occurred on rest days. The _Cov_ on a non-work day was obviously lower and more stable than on a work day. 3. Different bus stops. The small headway caused by former bus stops is continuously transmitted to the following buses. Bus bunching is transitive, and bus stops away from the departure place are more likely to be affected. The spatial attributes of the bus stop should be of great concern. Considering the spatial and temporal characteristics of bus bunching discussed above, twelve potential factors related to the following aspects were selected as the input vectors of the model: the location of the bus stop, the bus dwelling time at the former stop, the bus traveling time between bus stops, the bus headway at former stops, the departure interval, the type of the day, and the day of a week. A hybrid model integrating GA and DT was constructed to identify the significant factors of bus bunching. eGA was adapted to build the overall framework for factor identification, and the strategy of elitist preservation was used to avoid destroying the best-fitness individual from crossover and mutation. Each potential factor was encoded as a gene, and each factor group was regarded as a chromosome. Significant factor identification was transformed into a problem of selecting the fittest chromosome from the population. DT was utilized to select the individual with the greatest fitness. The proposed hybrid eGA-DT model outperformed the single DT and ET models in different situations:  
1. Different bus stops. eGA-DT outperformed at all bus stops, especially compared to ET. The average reduction in MAE by eGA-DT at each bus stop was about 5% compared to DT. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Type** & **eGA-DT** & **DT** & **ET** \\ \hline morning peak & 41.19 & 43.70 & 123.46 \\ evening peak & 50.67 & 55.63 & 168.30 \\ off-peak & 35.09 & 39.33 & 118.37 \\ rest day & 35.65 & 39.19 & 130.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MAE of eGA–DT, DT, and ET for different types of periods. Different bus routes. Four types of routes, including the regular, main, express, and branch route, were selected. The results showed that eGA-DT performed better for all types of bus routes, especially for branch routes with 23.54 in MAE. Moreover, eGA-DT outperformed DT and ET with an average reduction of 26% and 43% in MAE, respectively. 3. Different periods. Considering four types of periods, including morning peak on weekdays (7:00-9:00), evening peak on weekdays (17:30-19:30), off-peak on weekdays, and rest days, the MAE in peak periods was obviously larger than off-peak periods. eGA-DT outperformed DT and ET with an average reduction of 7.3% in MAE during peak periods and 10% during off-peak periods.