## 3 Methodology
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors (Year)** &
\begin{tabular}{c} **Country/Region** \\ **Being Investigated** \\ \end{tabular} & **Method** & **Contributions** & **Gap** \\ \hline
[17] & Europe & Model based on a general index of technical change & Shows the way to explore the distribution of productivity growth & No attempt to evaluate the determinants of productivity growth. \\ \hline
[18] & China & Multilevel grey evaluation (MGE) and TOPSIS & Overall evaluation of passenger transfer performance at large transport terminals under a robust and flexible method & Focus on the transfer performance and can be extended for the evaluation of overall performance. \\ \hline
[2] & Brazil & The methodology combined TOPSIS with a genetic algorithm to build optimized scenarios & Tobit regression to analyze the determinants of explanatory explanatory explanatory explanatory & Limited factors investigated in the second-stage analysis. \\ \hline
[19] & Worldwide & Meta-analysis followed by a permutation test & Evaluate the drivers of efficiency though systematically reviewing related literature studies & No actual procedure/method applied to estimate the performance. \\ \hline
[20] & Worldwide & Network DEA & The method represents both production and consumption technologies in a unified framework & The study focused on inter-relationships among performance indicators with no performance \\ \hline
[21] & Worldwide & DEA and translog production function & Construct DEA CRS and VRS measures and test their relationships with total factor productivity change & No examination regarding the determinants of efficiency/ productivity. \\ \hline
[22] & Worldwide & Multi-activity network DEA & Decomposition of overall efficiency into passenger technical efficiency, freight technical efficiency and technical effectiveness & There is no second-stage analysis related to the influencing factors of different performance indicators. \\ \hline
[23] & Taiwan & Two-stage centralized DEA & The adoption of two-stage centralized DEA as well as the concept of resource allocation & The paper focused on inner production process and lack of investigation on the external environment. \\ \hline
[24] & China & Trans-logarithmic total cost function & Answer the question whether Vertical Separation (VS) based reform can reduce the operation cost. & More policy/managranding implications can be derived from a further second-stage analysis. \\ \hline
[25] & Turkey & \
\begin{tabular}{c} An integrated hierarchical fuzzy \\ MCDM method is proposed based on FAHP and fuzzy-Choquet integral \\ \end{tabular} &
\begin{tabular}{c} Analyze and evaluate customer \\ satisfaction level in a healthy manner \\ through survey study, statistical analysis and multi-criteria \\ analysis and multi-criteria \\ decision-making \\ \end{tabular} & The study focused on the estimation of customer satisfaction level of performance. \\ \hline
[26] & China & Resampling DEA and the potential merger gains model & \
\begin{tabular}{c} Pre-evaluate the efficiency gains of \\ three representative M and A \\ schemes (i.e., regional M and A, \\ megamegarage, and a coalition between \\'strong' and 'weak' railway bureaus) \\ \end{tabular} &
\begin{tabular}{c} Only focus on the impact of \\ mergers and acquisitions on \\ efficiency, with no effort shown to \\ assess other factors. \\ \end{tabular} \\ \hline
[27] & Turkey & Interval type-2 fuzzy MCDM, TOPSIS and GRA & Type-2 fuzzy sets are more suitable to represent uncertainties & The investigation can be rather than focusing on customer satisfaction. \\ \hline
[28] & Turkey & SERVQUAL, interval type-2 fuzzy sets and VIKOR. & The interval type-2 fuzzy sets and VIKOR integration reveals and solves ambiguity and uncertainty in a more realistic way & The investigation can be generalized to overall performance rather than focusing on customer satisfaction. \\ \hline
[29] & China & \begin{tabular}{c} A two-dimensional \\ clustering method \\ Non-radial Data \\ Envelopment Analysis \\ \end{tabular} & \
\begin{tabular}{c} Proposal of the clustering method in \\ measuring railway performance \\ New approach/methodology in \\ analyzing safety at RLS at different levels \\ \end{tabular} &
\begin{tabular}{c} Causal effect is not shown \\ in the method. \\ No second-stage analysis regarding \\ the influencing factors \\ of performance. \\ \end{tabular} \\ \hline
[31] & UK & Investigate the human performance in the railway system &
\begin{tabular}{c} Develop Human Performance \\ Railway Operational Index through integrating the analytical network process and success likelihood \\ index methodology \\ \end{tabular} & The findings are restricted to performance and its influencing factors from human perspective. \\ \hline
[32] & Japan & \begin{tabular}{c} Lifecycle costing together with \\ reliability, availability, maintainability and safety \\ \end{tabular} & \
\begin{tabular}{c} Propose a key performance indicator \\ for railway track \\ \end{tabular} &
\begin{tabular}{c} No investigation into the overall \\ performance of the railway \\ operator/industry. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}