Wang, Z.;                     Li, S.;                     Li, Y.;                     Liu, D.;                     Liu, S.;                     Chen, N.    
        Investigating the Nonlinear Effect of Built Environment Factors on Metro Station-Level Ridership under Optimal Pedestrian Catchment Areas via the Machine Learning Method. Appl. Sci. 2023, 13, 12210.
    https://doi.org/10.3390/app132212210
    AMA Style
    
                                Wang Z,                                 Li S,                                 Li Y,                                 Liu D,                                 Liu S,                                 Chen N.        
                Investigating the Nonlinear Effect of Built Environment Factors on Metro Station-Level Ridership under Optimal Pedestrian Catchment Areas via the Machine Learning Method. Applied Sciences. 2023; 13(22):12210.
        https://doi.org/10.3390/app132212210
    
    Chicago/Turabian Style
    
                                Wang, Zhenbao,                                 Shihao Li,                                 Yongjin Li,                                 Dong Liu,                                 Shuyue Liu,                                 and Ning Chen.        
                2023. "Investigating the Nonlinear Effect of Built Environment Factors on Metro Station-Level Ridership under Optimal Pedestrian Catchment Areas via the Machine Learning Method" Applied Sciences 13, no. 22: 12210.
        https://doi.org/10.3390/app132212210
    
    APA Style
    
                                Wang, Z.,                                 Li, S.,                                 Li, Y.,                                 Liu, D.,                                 Liu, S.,                                 & Chen, N.        
        
        (2023). Investigating the Nonlinear Effect of Built Environment Factors on Metro Station-Level Ridership under Optimal Pedestrian Catchment Areas via the Machine Learning Method. Applied Sciences, 13(22), 12210.
        https://doi.org/10.3390/app132212210