###### Abstract  
When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. urban rail transit; long-term passenger flow forecast; ArcGIS; ANN +
Footnote †: journal: Medicaluse along the line, traffic connection, rail transit service level, and fare policy on urban rail transit passenger traffic and found that the passenger flow of a single rail transit line develops more slowly at the initial stage, and the cultivation period is longer [10]. [PERSON] et al. put forward a new method based on transfer information entropy (TIE), which is an indicator for the future long-term evaluation of the rail transit entropy (TIE) causal inference method used to determine the causal relationship between influencing factors and rail transit passenger flow, which can be used to derive the interaction between influencing factors and passenger flow after comparing with correlation analysis [11]. After analyzing passenger traffic and its influencing mechanism, various scholars have established models to predict the passenger flow. [PERSON] et al. divided the city into several areas with similar internal traffic attributes and moderate spatial scales to obtain more accurate influencing factors of passenger flow and proposed a passenger flow prediction model based on Xgboost for new lines in stations [12]. [PERSON] et al. proposed a model to predict the passenger flow of a new station based on the pattern of the station's historical passenger data and the attribute evaluation of the new station. After assessing the attributes of the stations, they proposed a passenger flow prediction method based on the attributes of the stations [13]. [PERSON] et al. used [PERSON]'s correlation analysis to study the intrinsic relationship between the built environment around the station and the passenger flow of the station and established a passenger flow prediction model with multilayer perceptron (MLP), and the results of their study can be used for the newly built rail stations that do not have historical passenger flow data [14]. [PERSON] et al. combined a Genetic Algorithm with an artificial neural network model and a locally weighted regression model to achieve highly accurate prediction results for short-term traffic flow on urban roads [15].Due to the unstable dependence of passenger flow in the long or short term, it is necessary to predict passenger flow using models that can capture different time series. [PERSON] et al. used mathematical and artificial neural network methods to predict the passenger flow of the subway based on the land use around the subway [16]. After a large number of validations, compared with other models, Long Short-Term Memory (LSTM) can effectively capture the long-term and short-term characteristics of traffic volume [17; 18; 19]. However, the selection of influencing factors in these studies is relatively one-sided and only focuses on whether some specific factors have an impact on the passenger flow of rail transportation, and it is difficult to achieve the effect of long-term prediction on the passenger flow of the new stations or the opening of the new line.