Nonlinear effects of built environment on urban rail transit station ridership

WANG Jing; DONG Chunjiao; SHAO Chunfu; WANG Mingzhi; WANG Junyue

doi:10.11860/j.issn.1673-0291.20240122

Abstract：To explore the nonlinear effects of the built environment on urban rail transit station rider-ship,this study takes Beijing's rail transit stations as its focus.Leveraging multi-source data,includ-ing Point of Interest(POI)data,mobile phone signaling data,and road network data,the built envi-ronment is finely characterized from four perspectives:socio-economic and demographic attributes,land use,multimodal connectivity,and station characteristics.A Gradient Boosting Decision Tree(GBDT)model is employed to reveal the relative importance,nonlinear influences,and threshold effects of these factors on station ridership across different time periods and travel directions.Results indicate that the GBDT model outperforms the Ordinary Least Squares(OLS)model,Adaptive Boosting(AdaBoost),and Random Forest(RF)models in terms of fitting performance.Socio-economic and demographic attributes exert the greatest influence on peak-period ridership,contribut-ing over 40%to both morning inbound and evening outbound peak-period ridership.Land use attri-butes have the strongest impact during off-peak periods,accounting for 33.06%and 49.10%of inbound and outbound ridership,respectively.The most influential variables exhibit pronounced non-linear and threshold effects on station ridership.Notably,stations located 15 to 22 km from the city center show significantly higher morning inbound and evening outbound ridership.Additionally,the proportion of passengers accessing rail transit via private cars is considerably higher during peak hours than during off-peak periods.These findings provide valuable theoretical support for the planning of urban rail transit networks and the spatial layout of urban land use.