## 6 Conclusions  
This paper uses neural network modeling to determine the factors affecting the long-term passenger flow of rail transit and the relationship between them. First of all, this paper uses ArcGIS to clearly demonstrate the distribution and numerical value of each factor and finds that the number of factors at each station is positively correlated with the size of passenger flow at that station. In addition, a rail passenger flow prediction model based on a class neural network is constructed using an SPSS Modeler. After running the model, the results are shown in the form of multiple line graphs and attributes of factors, with a prediction accuracy of 94.6%. After that, the Chaid tree model and the CART tree model are used for prediction, respectively, and the results show that the neural network-like model has a stronger reliability. Finally, the results of the model run also yielded a strong or weak relationship between these long-term influences and flow. This paper is able to demonstrate the interrelationship between several long-term factors and station passenger flow, predict the long-term passenger flow of current stations using the historical passenger flow data, and provide a strong reference for future urban planning and construction. The conclusions drawn are as follows:  
(1) Topological characteristics of the subway network, the environment of stations, and the connection of transportation modes around stations can influence the passenger flow of stations. These factors have a long-term influence on the passenger flow of stations. As Beijing is special in geographic location, ArcGIS can better reflect the distribution of various factors in each place and also highlight the special geographic location of Beijing;  
(2) The neural network method works well in predicting nonlinear data, such as spatio-temporal passenger flow. Due to the temporal and spatial characteristics of subway passenger flow, constant changes, and nonlinear characteristics, the artificial neural network used in this paper can make a good prediction on large-scale data processing and is easier to conduct;  
(3) Different factors have different influences on subway passenger flow. The convenience of connection by shared bicycles and buses has the biggest influence on subway passenger flow. Due to the special geographic location of Beijing, the proportion of industrial area and the proportion of residential area around rail stations have the least influence on passenger flow. This paper explores long-term factors affecting the passenger flow of rail transit and the correlation between them with the hope of providing guidance and suggestions for rail transit companies in planning and constructing new subway lines and stations, as well as configuring the surrounding transportation transfer tools and providing a reference basis for the construction of a TOD smart city. However, this paper also has many limitations, for which more in-depth research is needed. First, it does not consider the long-term impact of origin and destination (OD) in a specific region on the rail transit passenger flow. In the future, it is necessary to consider the starting and ending points of travelers so as to more accurately grasp the travel pattern of passengers and improve the prediction accuracy. Second, as the passenger flow of rail transit is complex and varied, we may need to divide travelers into different age groups so that we may be able to investigate and analyze the factors that affect their choice of transportation modes to improve the accuracy of prediction and provide richer and more reliable input variables for this study. Third, because this paper focuses more on exploring the mechanism between factors and passenger flow, the design of the model is not novel enough due to the limitation of the SPSS Modeler; in the future, we will design a superior combined model so as to achieve an all-around analysis and prediction.