# Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
## 1 Introduction
### Literature Review and Innovation of This Paper
[20] provided particular methodological insights into the influence of the chosen frontier method on the obtained efficiency results in the case of railways using a meta-analysis approach. [PERSON] et al. [21] explained the link between efficiency and effectiveness by DEA. [PERSON] et al. [22] evaluated the performance of the EWM-TOPSIS method considering four indicators including the global network efficiency. [PERSON] et al. [23] provided a new method based on EWM-TOPSIS to evaluate railway transport efficiency. [PERSON] et al. [24] evaluated the public transport efficiency of 11 cities in China by super-efficient network DEA. [PERSON] et al. [25] decomposed the efficiency into pure technical efficiency and scale efficiency and studied the spatial difference of efficiency. [PERSON] et al. [26] constructed the matrix of \"Operational Efficiency-Malmquist Index\" to comprehensively compare the advantages and disadvantages of urban rail transit operational efficiency. [PERSON] et al. [27] proposed a hybrid network data envelopment analysis (MNDEA) model with shared resources, a two-stage network and parallel structure integration. [PERSON] et al. [28] used the truncated regression two-stage bootstrap data envelopment analysis method to study the TOD development efficiency of Seoul transfer hub. [PERSON] et al. [29] benchmarked the performance of bus services in eight cities based on DEA. [PERSON] et al. [30] used stochastic data envelopment analysis (SDEA) and a Beta two-stage regression method to analyze different transportation modes in 285 cities from 2009 to 2012 and discussed their efficiency levels. [PERSON] et al. [31] established an index system consisting of 4 criteria and 24 indicators, covering key examination contents of bus operating performance. [PERSON] et al. [32] proposed a non-radial data envelopment analysis approach to measure operational and service efficiencies simultaneously. One of the key components of urban rail transit efficiency is transfer efficiency, which includes the transfer within the system and the transfers between the urban rail system and other modes of transport. [PERSON] et al. [33] pointed out that the transfer efficiency of passengers is affected by the degree of automation in the stations, multiple traffic modes and long transfer distances. [PERSON] et al. [34] explored the efficiency of transit stations using a robust bootstrap Data Envelopment Analysis. [PERSON] et al. [35] found that the three most important and potentially derivative transfer efficiency factors related to passengers include the impact on the comfort of the station's internal environment. [PERSON] et al. [36] pointed out that rail transit transfer efficiency of different groups of passengers would be affected by their travel purpose and monthly household income. [PERSON] et al. [37] established a comprehensive evaluation model of transfer efficiency based on the IEM-Vague. [PERSON] et al. [38] constructed a discrete-event simulation model of the organization of transfer trains. [PERSON] et al. [39] estimated the transfer efficiency by DEA and determined the factors affecting the efficiency by Tobit regression analysis. [PERSON] et al. [40] established a three-stage DEA evaluation index system for public rail transfer efficiency. With many achievements made in the theoretical research of transport efficiency, however, few have focused on railway and urban rail transit systems. Some research on the transport efficiency of the railway network adopted complex network theory and did not fully consider the capacity constraints of traffic nodes. Other studies combined dynamic factors such as capacity changes, operation plans and passenger demand, but only focused on one or two influencing factors, lacking studies on the global change and the case analysis for the whole network, which cannot provide a reference for passengers or operating units.