## 1 Introduction  
With the acceleration of urbanisation and the diversification of residents' travel needs, rail transit, as a high-capacity and high-efficiency urban transport mode, is playing an increasingly important role in easing urban traffic congestion and promoting coordinated regional sustainable development. At the same time, low-altitude economy, as an emerging mode of economic development, is gradually becoming an important force to promote urban economic growth and enhance the efficiency of urban management. Low-altitude economy not only involves new service forms such as drone delivery but also covers the demand for operating space for low-altitude vehicles, all of which bring new challenges and opportunities for urban built environment planning. Understanding and predicting rail traffic is crucial for optimising the allocation of public transport resources and improving operational efficiency and service quality. It is of great practical significance to clarify the relationship between built environment factors and ridership generation patterns and their influence mechanisms in order to optimise the planning layout of the rail transit system, improve operational efficiency, and ensure traffic safety. Therefore, this study takes the Shenzhen rail transit system as an example to explore the influence mechanism of built environment factors on ridership generation and distribution around stations, which will provide stronger theoretical support for the planning and effective management of urban public transportation systems and help realise a more intelligent and sustainable urban transportation optimisation scheme. However, the factors affecting the ridership of rail transit are complex and diverse, including both internal factors of rail transit system, such as train frequency and service quality, and external built environment factors, such as land use pattern, population distribution, house price level, and layout of public service facilities. The existing studies on the impact of the built environment on rail transit ridership have primarily focused on linear models and spatial heterogeneity [1; 2]. At the same time, these studies often overlook the nonlinear effects and the combined influence of internal and external environmental factors. Specifically, current research rarely considers the nonlinear effects of built environment factors in conjunction with spatial heterogeneity. Additionally, there is a lack of analysis using three-dimensional partial dependence plots. Taking Shenzhen as an example, this paper applies the MGWR model and GBDT model based on the automatic fare collection (AFC) card swipe records and a variety of built environment variables to explore the impact of different built environment factors on the ridership in and out of the rail transit stations under different research perspectives. In order to consider the spatial heterogeneity, this study firstly adopts a multiscale geographically weighted regression (MGWR) model to visualise the spatial heterogeneity of built environment factors on inbound and outbound ridership. A machine learning model, Gradient Boosted Decision Tree (GBDT), was introduced with the aim of exploring the nonlinear relationship of the factors influencing ridership in rail transit. The marginal effects between the independent variables and the dependent variables are demonstrated through partial dependency diagrams (PDP), with the aim of comprehensively analysing the influence mechanism of rail transit ridership and providing scientific basis and technical support for relevant policy formulation.