# Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
## 4 Result Analysis
### Analysis Conclusion  
To sum up, the following conclusions can be drawn:  
1. A small change in passenger flow has no significant impact on the per capita travel time of passengers.
2. The shadow efficiency of increasing the departure density of crowded lines in morning rush hour is the highest, far exceeding that of other schemes, so it should be adopted first.
3. The greater the passenger volume, the more obvious the efficiency improvement brought by increasing the departure density.
4. The departure interval is the restriction of technical efficiency in the combination strategy, which can be increased to optimize the technical efficiency of all schemes.
5. The newly built Line 6 can improve the scale efficiency of the system.
6. The newly built Changing Line cannot improve the scale efficiency of the system.
7. In combination optimization, under the conditions of original passenger flow and original departure interval, the shadow efficiency of the two schemes of removing the Changing Line and Line 6 is inconsistent with that of the single factor simulation experiment, and the relationship between shadow efficiencies also changes, which proves that the shadow efficiency is marginal efficiency, which is related to the starting point of the original scheme.
8. According to the current cost calculation methods and parameter values, the cost of Beijing subway lines accounts for the vast majority of the total cost, which has a great influence on the final calculation and evaluation results of efficiency.  
It can be further concluded from the simulation results that the expensive construction cost of Beijing subway is an important factor that restricts the improvement of network transport efficiency. We should try our best to control the cost of newly built lines, so as to improve the passenger service level by providing a larger-scale rail network under the constraint of a certain efficiency level. Compared with the fixed costs such as the construction costs, the variable cost such as running kilometer is low, which means that the existing line capacity should be brought into full play. For example, passenger service level and transport efficiency can be improved by increasing the marshaling and shortening the departure interval. Last but not least, compared with the relatively expensive infrastructure reconstruction scheme, operation organizations with less investment should be given priority to improve transport capacity and efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**DEA Evaluation Results**} & \multicolumn{3}{c}{**Network Efficiency**} \\ \hline
**Optimum Scheme** & **Technical Efficiency** & **Pure Technical Efficiency** & **Scale Efficiency** & **Scale Reward** & **Input 1** & **Output 2** & **Shadow Efficiency** \\ \hline
3 & 1 & 1 & 1 & unchanged & 11,310,210 & 1,799,820 & 0.15913 \\
6 & 0.971652 & 1 & 0.971652 & decreased by degrees & 11,273,050 & 1,684,830 & 0.14945 \\
9 & 0.542965 & 1 & 0.542965 & decreased by degrees & 37,162.8 & 154,330 & 4.15283 \\
12 & 0.485527 & 1 & 0.485527 & decreased by degrees & 0 & 0 & - \\
15 & 0.620231 & 0.999822 & 0.620342 & increased by degrees & 4,686,273 & 402,320 & 0.08858 \\
18 & 0.573691 & 1 & 0.573691 & decreased by degrees & 4,649,110 & 274,240 & 0.05898 \\
21 & 0.949969 & 1 & 0.949969 & decreased by degrees & 6,661,103 & 1,474,690 & 0.22138 \\
24 & 0.92026 & 1 & 0.92026 & decreased by degrees & 6,623,940 & 1,387,770 & 0.20950 \\ \hline \hline \end{tabular}  
* The input refers to the increased cost (unit: CNY) compared with the benchmark scheme. 2 The output refers to the saved travel time (unit: CNY) compared with the benchmark scheme.  
\end{table}
Table 11: DEA evaluation results of each program for a 10% decrease in passenger flow scale.