## 2 Literature Review
When the price of transfer tickets was discounted, the transfer ridership will keep increasing [49]. Reasonably coordinating the relationship between land-use attributes and pricing policies could improve the proportion of intermodal travel [50; 51; 52]. The network attribute and location of transfer hubs have a certain impact on the ridership prediction. Closeness centrality was an important measure of station connectivity [53]. It was one of the important factors to predict the bus-metro-transfer ridership [54]. Research [30] found that closeness centrality was positively related to bus-metro-transfer ridership. On the other hand, previous research results on the impact of distance to CBD on metro ridership were inconsistent. A paper [55] found that the distance to CBD had no significant impact on the passenger flow. By contrast, some studies found that the ridership of metro stations in CBD was much higher than other stations, in Nanjing [9]. The demographic factors, also, affect transport-mode choices to serve distinct trip purposes [56]. Some previous studies examined the effects of demographic characteristics on passenger flow [25; 57]. Demographic characteristics, such as age, per capita GDP, and car ownership were found to affect residents' travel choices [26; 48]. The influence of the built environment on transit ridership is not fully explored, although many studies are focused on this topic. For example, most previous studies, mainly, applied linear-regression models to examine the relationship between the built environment and transit ridership [8; 9]. However, in the actual prediction process, if built-environment variables have nonlinear and threshold effects on transit ridership, the application of linear models will distort the research results [58]. Some previous studies confirmed that the built environment has a nonlinear impact on travel behavior [28; 46; 59]. Progress of machine-learning and interpretation methods can better help us understand the nonlinear and regulatory effect of the built environment on human travel behavior [24; 27]. The advantage of the nonlinear model, highlighted by [60; 61], showed that the methods can break through the limits of samples, to produce a more reliable and stable prediction. Besides, the methods can capture complicated correlations between independent variables and dependent variables because there are no limits on the pattern/shape of the relationship [62]. The nonlinear effects verified that the marginal influence of the built-environment variables on travel behavior depended on the value of the built-environment variables. The threshold effect was one of the nonlinear effects. When the marginal influence of built environment variables on travel behavior exceeded a certain threshold, the value of the dependent variable increased, decreased, or remain unchanged [63]. In recent years, scholars are more and more interested in the application of tree-based machine-learning methods (e.g., random forest, LightGBM, and XGBoost), to identify the nonlinear and moderating impact of the built environment on travel behaviors [7; 45; 46; 64; 65; 66; 67]. Compared with the linear model and other commonly used nonlinear models (e.g., random forest and LightGBM), XGBoost had a higher degree of fitting the data [68]. XGBoost was a scalable end-to-end tree-boosting system, which can provide the most accurate research results for many problems, and it solved real-world-scale problems with the least resources [69]. The XGBoost model had the advantage that the prediction results were more in line with the actual situation, and the results were easily explainable [41; 70]. XGBoost, also, can further reveal the relative importance and practical significance of relevant independent variables.In this paper, based on the Shanghai metro-smart-card data, the XGBoostmodel was used to explore the influence of urban built-environment variables on transfer ridership.