## 8 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**City**} & \multirow{2}{*}{**Degree**} & \multirow{2}{*}{**Degree**} & \multirow{2}{*}{**Betweenness**} & \multirow{2}{*}{**Closeness**} & \multirow{2}{*}{**Eigenvector**} & \multirow{2}{*}{**Constraint**} & \multirow{2}{*}{**Custering**} & \multirow{2}{*}{**Comprehensive**} & \multirow{2}{*}{**Score of Railway**} & \multirow{2}{*}{**Ranking**} \\  & & & Centrality & Centrality & Centrality & & & Coefficient & Coefficient & & \\ \hline
21 & Dongguan & 3 & 0.06767 & 0.00000 & 0.472527 & 0.036872 & 0.397977 & 1.000000 & 0.450085 & 40 \\
22 & Nanjing & 20 & 0.46516 & 0.040724 & 0.651515 & 0.184946 & 0.118198 & 0.436842 & 0.492756 & 11 \\
23 & Xining & 2 & 0.046512 & 0.000000 & 0.430000 & 0.022584 & 0.551827 & 1.000000 & 0.457530 & 34 \\
24 & Tianjin & 25 & 0.581395 & 0.065271 & 0.704918 & 0.215798 & 0.113469 & 0.400333 & 0.495618 & 9 \\
25 & Taiyuan & 14 & 0.325881 & 0.004659 & 0.537333 & 0.150739 & 0.150603 & 0.637363 & 0.467714 & 21 \\
26 & Shenzhen & 17 & 0.395349 & 0.021504 & 0.623188 & 0.169303 & 0.133101 & 0.514706 & 0.484054 & 12 \\
27 & Jinan & 19 & 0.441860 & 0.032107 & 0.641791 & 0.168003 & 0.122759 & 0.438966 & 0.483260 & 15 \\
28 & Shanghai & 20 & 0.465116 & 0.032380 & 0.651515 & 0.194503 & 0.115797 & 0.457895 & 0.493701 & 10 \\
29 & Tangshan & 6 & 0.139535 & 0.000929 & 0.472527 & 0.045667 & 0.269930 & 0.733333 & 0.455568 & 36 \\
30 & Shijiazhuang & 10 & 0.232586 & 0.001965 & 0.51282 & 0.105552 & 0.175928 & 0.666670 & 0.45887 & 31 \\
31 & Chengdu & 22 & 0.511628 & 0.612648 & 0.617875 & 0.232521 & 0.129773 & 0.588745 & 0.508949 & 1 \\
32 & Yinchuan & 3 & 0.009767 & 0.000143 & 0.462366 & 0.030719 & 0.384680 & 0.666667 & 0.432768 & 42 \\
33 & Nanning & 15 & 0.348837 & 0.006503 & 0.616534 & 0.163758 & 0.154230 & 0.666667 & 0.481669 & 16 \\
34 & Hohhot & 17 & 0.395349 & 0.044308 & 0.614286 & 0.139883 & 0.114371 & 0.34588 & 0.470704 & 25 \\
35 & Hangzhou & 12 & 0.279700 & 0.011596 & 0.544304 & 0.103557 & 0.151506 & 0.439994 & 0.466891 & 28 \\
36 & Fuzhou & 7 & 0.162791 & 0.005679 & 0.511905 & 0.54375 & 0.174249 & 0.283095 & 0.498890 & 43 \\
37 & Guiyang & 10 & 0.232558 & 0.001753 & 0.524390 & 0.105467 & 0.196212 & 0.758556 & 0.472797 & 23 \\
38 & Beijing & 15 & 0.348877 & 0.014928 & 0.597222 & 0.143288 & 0.133332 & 0.485714 & 0.471305 & 24 \\
39 & Xuzhou & 8 & 0.186047 & 0.003215 & 0.524390 & 0.072299 & 0.194695 & 0.535714 & 0.452524 & 39 \\
40 & Quanzhou & 5 & 0.116279 & 0.002304 & 0.518072 & 0.046195 & 0.260505 & 0.60000 & 0.427987 & 44 \\
41 & Jijiang & 5 & 0.116279 & 0.000123 & 0.483146 & 0.052814 & 0.289606 & 0.900000 & 0.485679 & 32 \\
42 & Yantai & 6 & 0.139535 & 0.000154 & 0.50000 & 0.708838 & 0.242848 & 0.86667 & 0.467560 & 27 \\
43 & Liuzhou & 8 & 0.186047 & 0.001409 & 0.524390 & 0.090434 & 0.203046 & 0.714286 & 0.461740 & 30 \\
44 & Wuxi & 6 & 0.139535 & 0.001638 & 0.505882 & 0.049560 & 0.242902 & 0.600000 & 0.440172 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No. ** & **City** & **Degree** & \begin{tabular}{c} **Degree** \\ **Centrality** \\ \end{tabular}  & \begin{tabular}{c} **Betweenness** \\ **Centrality** \\ \end{tabular}  & \begin{tabular}{c} **Closeness** \\ **Centrality** \\ \end{tabular}  & \begin{tabular}{c} **Eigenvector** \\ **Centrality** \\ \end{tabular}  & \begin{tabular}{c} **Constraint** \\ **Coefficient** \\ \end{tabular}  & \ \begin{tabular}{c} **Clustering** \\ **Coefficient** \\ \end{tabular}  & \begin{tabular}{c} **Comprehensive Score** \\ **of Highway Network** \\ \end{tabular}