## 6 Discussion  
The urban public transport scheduling problem is a very complex problem, and the calculation process is cumbersome and involves a wide range. In this paper, the influence of bus exhaust emissions is considered, and the bus dispatching model is established. Based on the green concept, the bus scheduling model is optimized to obtain a bus scheduling optimization model with the goal of minimizing the operating cost of the bus company, minimizing the loss of passenger benefits, and minimizing the cost of exhaust emissions. The optimal scheduling scheme of a line is obtained by solving an example. In future scheduling research, the following factors can also be considered:  
The number of operating vehicles, passenger waiting time, traffic venue distribution, bicycle ownership, GDP, resident population, urban road mileage, bus operation line length, number of civil motor vehicles, average fare, and other factors that may impact urban public transport. Discuss the interrelatedness of these factors and their cumulative impact on the efficiency and sustainability of public transport systems. The integration of green concept in bus scheduling: explore the significance of incorporating environmental factors (such as bus exhaust emissions, and the impact of vehicle driving state on exhaust emissions) into the bus scheduling model. Exploring how to optimize the bus schedule based on the green principle can not only reduce environmental pollution but also helps bus companies save costs and improve the overall passenger experience. The combination of multi-objective optimization methods: This paper expounds on the challenges brought by the multi-objective characteristics of the urban public transport scheduling problem, in which the key objectives are to minimize the operating cost, passenger travel cost, and environmental impact. In this paper, a single genetic algorithm is used to solve the model. In future research, the combination of genetic algorithm and optimization methods such as simulated annealing algorithm, ant colony algorithm, and particle swarm optimization algorithm can be considered to improve the effectiveness of solving these complex optimization problems and achieve a balanced solution considering various conflict objectives. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Time Period** & **Departure Interval** & **Departure Number** \\ \hline
1 & 6:30–7:30 & 15 & 4 \\
2 & 7:30–8:30 & 10 & 6 \\
3 & 8:30–9:30 & 15 & 4 \\
4 & 9:30–10:30 & 20 & 3 \\
5 & 10:30–11:30 & 20 & 3 \\
6 & 11:30–12:30 & 20 & 3 \\
7 & 12:30–1:30 & 15 & 4 \\
8 & 1:30–2:30 & 20 & 3 \\
9 & 2:30–3:30 & 20 & 3 \\
10 & 3:30–4:30 & 20 & 3 \\
11 & 4:30–5:30 & 15 & 4 \\
12 & 5:30–6:30 & 10 & 6 \\
13 & 6:30–7:30 & 15 & 4 \\
14 & 7:30–8:30 & 20 & 3 \\
15 & 8:30–9:30 & 20 & 3 \\
16 & 9:30–10:30 & 20 & 3 \\ \hline \end{tabular}
\end{table} 
Table 10: Adjusted departure interval and departure number. \begin{table}
\begin{tabular}{c c c c} \hline
**Passenger Waiting Time** & **Bus Company’s Operating** & **Bus Company’s Exhaust** \\
**Cost (EUR/d)** & **Cost (EUR/d)** & **Emission Cost (EUR/d)** \\ \hline Pre-optimization & 1735.79 & 712.19 & 880.30 \\ Sub-optimal bus departure plan & 1640.42 & 656.55 & 811.53 \\ Reduce percentage & 5.49\% & 7.81\% & 7.82\% \\ \hline \end{tabular}
\end{table} 
Table 11: Cost comparison between optimal and suboptimal bus departure plan. Verification analysis and practical application: This paper provides insights for the verification and analysis of passenger flow data in specific cities (such as Nanjing). How to verify the effectiveness of the proposed bus scheduling optimization model by discussing the results of this practical application. Future research directions should emphasize the practical relevance of research results and their implementation potential in different urban environments.