## 2 Literature Review
### The Selection of Built Environment Factors Affecting Ridership  
In recent years, scholars at home and abroad have carried out extensive research on the influencing factors and influencing mechanisms of rail traffic flow. The factors affecting the ridership of rail transit mainly include internal and external factors of rail transit system. A large number of studies have proved that the influence of external influencing factors on ridership of rail transit system is generally greater than that of internal influencing factors [1; 2]. Built environment factors are the main external factors of public transport systems, which have a significant impact on urban public travel behaviour [3; 4]. Built environment influences are divided into three main categories: socioeconomic factors, land use factors and station characteristics [5]. In terms of socioeconomic factors: [PERSON] et al. (2023) [6] used a geographically weighted regression model (GWR) to analyse the spatial heterogeneity of the factors influencing morning peak ridership in the Shenzhen Metro by taking into account the factor of house prices and found that the factor of house prices had a significant negative correlation with the commuter ridership [7]. In terms of land use factors, [PERSON] et al. (2022) [8] used the MGWR model to analyse the built environment variables within the Beijing metro buffer zone from category '5D' to category '7D', and found that office facilities, catering facilities, floor area ratio (FAR), number of car parks, number of parking lots, number of buses, and number of buses in the metro buffer zone were significantly negatively correlated with commuter flow [9]. It was found that office facilities, food and beverage facilities, floor area ratio (FAR), number of car parks, and whether or not the underground station is interchangeable are all significantly correlated with the outbound ridership of the station. In terms of station characteristics, [PERSON] et al. (2023) [10] considered station characteristics, such as bus routes, interchange stations, car parks, terminals, intercity stations, etc. and used a geographically weighted regression (GWR) model to compare the effects of built environment factors on rail traffic flow under different calendar events in the Shenzhen Metro. The results showed that the traffic flow during different calendar events showed the following pattern: unofficial holidays (Valentine's Day) > weekdays > weekends > public holidays (Spring Festival).  
As an integrated learning method, GBDT can not only effectively fit complex nonlinear relationships, but also provide an assessment of the importance of variables and help to identify the most significant influencing factors. GBDT models illustrate the relationship between independent and dependent variables by generating partial dependency graphs, while showing the marginal effect of the independent variables' characteristics on the -dependent variables [11]. Currently, the GBDT model is used to analyse the impact of single built environment influences on ridership mainly using two-dimensional partial dependency plots. Although [PERSON] et al. (2024) [9] and [PERSON] et al. (2024) [12] compared the fit of linear (MGWR model) and nonlinear models (GBDT model, XGBoost) respectively, confirming the advantage of nonlinear models, there are relatively few studies on the analysis of three-dimensional partial dependency graphs under the influence of two-factor linkage, which cannot consider multidimensional the influence of built environment factor ridership.