## 1 Introduction  
As the global urbanization process continues to advance, rail transit systems have become a key means to alleviate urban traffic congestion, improve travel efficiency, and promote sustainable urban development. In China, in large cities like Shenzhen, rail transit plays a crucial role in carrying the demand for short-distance commuting and cross-city travel within the city. However, with the increasing improvement in the rail transit network, the spatial distribution characteristics of passenger flow and its complex interaction with the built environment around the stations have become increasingly important research topics in the field of transportation planning and management. Therefore, this study focuses on the Shenzhen rail transit system, exploring the influence mechanism of built environment factors around rail transit stations on the generation and distribution of passenger flow, which will provide theoretical support and a decision-making basis for the scientific planning and efficient management of urban transportation systems. At present, the research on the influence mechanism of built environment factors on the passenger flow of rail transit stations mainly focuses on analyzing influencing factors [1,2] and methods [3]. Influencing factors mainly include socio-economic and land use diversity [4]. Housing prices, as an important socio-economic influencing factor, affect the distribution and density of the urban built environment, which indirectly affects the generation of rail transit passenger flow. Meanwhile, land use diversity is a more commonly used built environment influencing factor for exploring transit patronage. In previous studies, POI data have often been used to describe land diversity for passenger flow prediction [5] or to analyze the spatial heterogeneity of the influencing factors [6; 7]. Some studies have also used POI data to validate the accuracy of the model, but few studies have used the spatial characteristics of the influencing factors of the rail traffic flow to further analyze the urban zoning policy. Regarding research methodology, many current models examine how built environment factors affect passenger flow at rail transit stations. The four-stage model is a widely used method [8; 9] in urban traffic flow prediction, which studies the traffic volume in the global scope, and the workload is large, it is difficult to respond to the changes in the built environment around the station in time, and the model prediction results are inaccurate. Although neural network models [10; 11] are suitable for dealing with large-scale, high-dimensional datasets due to their strong fitting abilities, they face challenges in achieving good results because they require a large amount of high-quality data for training, and their high costs make medium- and long-term predictions challenging. At present, the OLS (Ordinary Least Squares) model considering global regression is also commonly used in such research [12; 13]. Such models, while providing an analysis of overall trends, fail to adequately account for spatial heterogeneity in spatial parameters. In fact, the degree of influence of the influencing factors is not necessarily the same for different rail transit stations [14]. In this paper, we address the shortcomings of traditional models in analyzing spatial heterogeneity by introducing a geographically weighted regression (GWR) model [4; 15]. The GWR model takes into account the spatial instability of passenger flow, and the model has been widely used in rail transit passenger flow prediction [16]. Therefore, it can be used to study the impact of built environment factors on passenger flow at urban rail transit stations. Therefore, this paper focuses on the Shenzhen rail transit system as the research object and aims to analyze the influence of built environment factors on the passenger flow of rail transit stations. First, spatial autocorrelation tests and multicollinearity tests were conducted on the independent variables to ensure the validity of the model inputs.