## 5 Discussion  
Previous studies on rail transit passenger flow forecasting focus more on model innovation and the lack of in-depth analysis of the input data, such as factors affecting long-term rail transit passenger flow, the network structure and distribution of stations, and the interaction between stations. Even though certain previous studies on related topics have chosen influencing factors that are more similar to those in this paper, they have not visualized the specifics of each influencing factor, nor have they provided a more objective and comprehensive explanation of why certain regional rail stations have a large distribution of that influencing factor. In addition, compared with previous studies, this paper also takes a more long-term perspective to explore the effect of how these factors have a long-term impact on passenger flow.  
For example, this paper analyzes the reasons for the high number of bus stops in the eastern part of Batop Line through fieldwork. Combined with the specificity of Beijing's geographic location and the inner thoughts of the working population, this phenomenon will continue for a long time, and it is difficult to make temporary changes to it. Therefore, the number of bus stops in this area has a long-term impact on rail transit passenger flow. Various types of building facilities and existing subway stations will not be demolished in a short period of time, so they have a long-term effect on rail transit passenger flow in the neighborhood. After summarizing the importance of each influencing factor on passenger flow, this paper objectively and comprehensively analyzes why the number of shared bicycles and the proportion of residential areas are the most and least important  
Figure 15: The order of importance of influencing factors.  
factors affecting the passenger flow of rail stations. Finally, this paper also divides the attraction features into different types of building facilities, visualizes them separately, and explains why they affect subway passenger flow.  
This paper proposes that long-term factors affecting passenger flow of rail stations depend on metro network structure and the external environment of the stations and finds that the correlation between them is clearly demonstrated through visualized analysis. In terms of the metro network, when a new station is added, the increase in network nodes will destabilize the original network, leading to changes in passenger flow. In terms of the external environment of stations, different types of facilities will generate passenger flow, and various means of transportation around the stations also provide travelers with transport convenience. Therefore, it is necessary to have a comprehensive consideration of network topology characteristics and the external environment on passenger flow. This paper achieves over 90% accuracy in predicting passenger flow using SPSS Modeler software 18.0 and class neural networks, indicating that neural networks work well for nonlinear data prediction.  
Therefore, in view of the fact that new rail stations require long-term planning for the next few decades, it is very necessary to analyze the factors that will have a long-term impact on passenger flow, especially for certain special areas, in order to provide substantial reference recommendations for the construction of rail stations.