## 6 Conclusions  
This paper applied the XGBoost model to study the nonlinear impact of the Shanghai built environment on bus-metro-transfer ridership. The results put forward reasonable suggestions for urban architectural planning and TOD development. Firstly, we carried out the correlation analysis of the independent variables and eliminated the adverse impact of the multicollinearity of the data on the model. Then, the new dataset was substituted into the XGBoost model, to obtain the relative importance rank of independent variables, for predicting transfer ridership. Finally, we analyzed the nonlinear effects of independent variables on transfer ridership. The results indicate that the bus-metro connection characteristics (relative importance is 53.77%) have the largest impact on bus-metro-transfer ridership. The four key variables are bus-network density (relative importance is 27.56%), bus-stop density (relative importance is 17.27%), network repetition ratio of bus-metro (relative importance is 8.49%), and average transfer distance (relative importance is 0.45%). These findings are useful for the bus-metro-transfer-facilities design of the metro-station areas. A station with higher closeness centrality can attract more transfer ridership. This means that the bus-metro-transfer facilities should be improved at the central stations. The land-use attributes for trip attraction have an important impact on bus-metro-transfer ridership. Among them, the commercial ratio has the most significant influence, with a relative importance of 3.28%, followed by the employment density, with a relative importance of 1.97, and the industrial ratio, with a relative importance of 0.14%. These findings are useful for TOD planners to establish land-use guidelines [7, 44]. The research results of this paper can better help urban planners and transportation departments understand how the change of urban built environment affects the bus-metro-transfer ridership. The increase in the bus-network density, in areas without the provision of metro service, can provide travel convenience for residents. The employment density and commercial ratio have positive impact on the transfer ridership. To increase the mobility of the travel population and improve transfer ridership, the concentrated business circle around metro stations should be appropriately arranged. Compared with the traditional models, the XGBoost model provides a more accurate and stable data-processing method. It can conduct integration analysis with various factors, in the presence of big data. There are, however, some limitations in this study. For example, this paper only uses the one-day IC-card data of Shanghai's 88 metros. In fact, it can further accurately conduct the impact of the built environment on bus-metro-transfer ridership, through the continuous time data of multiple cities. Most of the independent variables selected in this  
Figure 11: The influence of average transfer distance and closeness centrality on transfer ridership. paper are exogenous factors, due to limited resources. The passengers' travel attitude and travel preference are very important factors in the prediction process of transfer ridership. In the future, these endogenous factors can be taken into account. Conceptualization, D.L. and W.R.; methodology, D.L. and W.R.; software, D.L. and W.R.; validation, D.L. and J.Z. ; formal analysis, D.L. and W.R.; investigation, D.L. and W.R.; resources, Y.-E.G. ; data curation, D.L. and W.R.; writing--original draft preparation, D.L. and W.R.; writing--review and editing, D.L. and J.Z. ; visualization, D.L. and W.R.; supervision, Y.-E.G. ; project administration, J.Z. ; funding acquisition, D.L., J.Z. and Y.-E.G. All authors have read and agreed to the published version of the manuscript. The work described in this paper was jointly supported by the National Natural Science Foundation of China (Grant No. 72031005), the National Key R&D Program of the Ministry of Science and Technology of China (Grant No.