## 2 Analysis of Influencing Factors
### Distribution and Relevance of Influencing Factors  
In order to better reflect the distribution size of each influencing factor in each station from the whole, ArcGIS was utilized to map each influencing factor and indicate the quantity by the size of the graded symbols; the larger the point, the larger the quantity of the factor. At the same time, the SPSS Modeler was used to establish three-dimensional diagrams and matrix diagrams to visualize and analyze the correlation between each influencing factor and passenger flow. Connectivity properties. (1) Betweenness centrality (BC) [20; 21] is usually measured in terms of the number of points and edges, and BC solves the global problem of planning the optimal distances between stations to make the entire metro network balanced. The centrality of the median reflects the connectivity performance of the station, as well as the importance of the station, which, in turn, affects the ease of transferring passengers at that station. The easier it is for passengers to transfer, the more they save on travel costs, which indirectly attracts a large number of passengers to the station. As shown in Figure 2a, the number of points on Line 6 and Line 4 located within the third ring road of the city center and Line 14 in the eastern Chaoyang District are larger, indicating that the connectivity of these stations is stronger. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & **Data Source** & **Calculation Formula** \\ \hline \multirow{4}{*}{POI} & \multirow{4}{*}{Goddard map crawling POI facility points} & \(D_{i}=N_{i}/S_{i}\) \\  & & Di is the density of a facility around the station, \(Ni\) is the number of facility points \\  & & near the station, and \(Si\) is the area of the \\ \hline Number of connecting bus stops & Gaode map crawls the number of stops & \\ \hline Bicycles available for connection & Gaode map crawls pickup points & \\ \hline Population characteristics & Baidu Wise-Eye Population Data & \\ \hline Cluster centrality (CC) & Expressed as aggregation coefficient & \(C_{i}=\frac{2e_{i}}{k_{i}(k_{i}-1)}\) The clustering coefficient of node \(i\) with degree \(k_{i}\), where \(e_{i}\) is the number of edges between neighboring nodes of node \(i\) \\ \hline \multirow{4}{*}{Betweenness centrality (BC)} & \multirow{4}{*}{Node median} & \(BC_{i}=\sum\limits_{s\
eq i\
eq i}\frac{e_{i}^{s}}{S^{s}}\): \(n_{st}^{i}\) denotes the number of paths passing through node \(i\) and is the shortest path; \(S_{st}\) denotes the number of shortest paths connecting \(s\) and \(t\) \\ \hline Railway line network and stations & openmaptiles.org & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Influencing factors and their calculation methods. Figure 2: Connectivity characteristics. (**a**) Betweenness centrality. (**b**) Clustering centrality. (2) Cluster centrality (CC) [22; 23] is usually measured by the clustering coefficient, which is expressed as the ratio of the actual number of edges of a node in the network with the surrounding nodes to the maximum of the theoretical number of edges, reflecting the degree of aggregation of the subway network sites and being more concerned with the local characteristics. The higher the clustering centrality, the more concentrated the stations in the area. There are a large number of building facilities in the area, shortening the distance from the station to the destination for passengers, providing convenience for travelers, and thus indirectly attracting a large number of passenger flows. As shown in Figure 2b, the number of points is larger in the stations on Line 1, Line 2, Line 4, Line 5, and Line 6, which are located within the second ring road of the city center, indicating that when the clustering coefficients of these stations are larger, the denser the stations are in the area. After the new rail transit line is connected or the new station is built, the original network topology characteristics will be changed.