# Considering the Optimization Design of Urban Bus Network Scheduling
## 4 Example Analysis
### Case Study Introduction
#### 4.1.2 Parameter Settings  
The next step will be to construct a synchronized optimization model for the bus network and scheduling based on genetic algorithms, with a focus on determining the model and algorithm parameters. (1) Model Parameters  
In this study, the conversion coefficient \(\lambda\) is set to 0.5, as passenger time cost and public transportation operation cost are considered equally important during the optimization process. Based on the average salary in Guangzhou in 2021 of 10,843 yuan, the value of \(\omega\) is set to 36.1 yuan/h. To unify passenger time cost and public transportation operation cost in the objective function (Equation (13)) and avoid a significant gap between time cost and operation cost, both are converted into a daily cycle for research purposes. According to the Guangzhou Public Transport Development Annual Report in 2021, the average speed of city roads in the central urban area of Guangzhou during weekdays is 30.57 km/h, and the average speed of buses V is set to 30.57 km/h. Based on a single bus cost of 3 million yuan and an average bus service life of 15 years, the daily vehicle procurement cost \(\overline{\text{M}}\) is set to 548.1 yuan/vehicle. After analyzing the vehicle operation data for the selected routes, the average bus stop time \(\text{T}_{\text{s}}\) for the selected routes is set to 36 s. Therefore, the values of the parameters in the synchronized optimization model proposed in this study are as shown in Table 1 below: \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Emphasis coefficient \(\lambda\) & 0.5 \\ Time and cost conversion factor \(\omega\) & 45.1 mph/h \\ Average speed \(V\) & 30.57 km/h \\ Unit mileage consumption cost \(\overline{N}\) & 2.8/km \\ Average dwell time \(T_{s}\) & 36 s \\ Single – day vehicle acquisition cost \(\overline{M}\) & 548.1/vehicle \\ Minimum route length \(l_{min}\) & 10 km \\ Maximum route length \(l_{max}\) & 30 km \\ Minimum departure interval \(f_{min}\) & 5 min \\ Maximum departure interval \(f_{max}\) & 15 min \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synchronized optimization model parameters. (2) Algorithm Parameters  
Based on the actual situation of selecting the line network in this case, and determining the decision variable dimension of this case as 130 based on the above analysis, the relevant parameters of the genetic algorithm are determined according to the synchronous optimization model characteristics of solving the line network of this case. The setting of the population size: Setting the number of populations too small may result in large errors, making the results unable to converge; setting it too large will increase the difficulty of solving the problem. Generally, the population size is set to be 20 to 100 times the decision variable dimension. In this case, the population size for the decision variable dimension of the line network of this case is set to 5000. Setting the number of iterations: Through multiple trial calculations, it was found that the objective values converged at around 150,000 iterations. To ensure the convergence of the results, this paper increases the number of iterations to 200,000. Setting the crossover probability: In order to achieve the single-point crossover method of exchanging only one gene segment as described in the previous chapter, since the number of gene segments for this line network is 5, the crossover probability \(P_{\text{c}}\) is set to 0.2. Setting the mutation probability: Generally, the mutation probability is set between 0.0001 and 0.1. In this paper, the mutation probability is set as \(P_{\text{m}}=0.01\). We mitigate the impact of invalid solutions by constructing a penalty function.