## 2 Literature Review

Table 1: Summary of reference literature on transportation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Analysis Methods** & \begin{tabular}{c} **Main Independent** \\ **Variables** \\ \end{tabular} & **Dependent Variables** & **PCA** & **Travel Mode** & **Analysis Area** \\ \hline [PERSON] et al. (2015) [22] & MGWR & land use and density,  socioeconomic & & & & & \\ \multirow{4}{*}{[PERSON] et al. (2019) [57]} & \multirow{4}{*}{GWR} & land use and density, & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2019) [21]} & \multirow{4}{*}{Gradient Boosting regression trees (GMRT)} & \multirow{4}{*}{GWR} & \begin{tabular}{c} socioeconomic \\ characteristics, \\ and density, rail \\ transit service \\ \end{tabular} & \begin{tabular}{c} **Dependent Variables** \\ \end{tabular} & **PCA** & **Travel Mode** & **Analysis Area** \\ \hline Jun et al. (2015) [22] & MGWR & land use and density, & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [23]} & \multirow{4}{*}{GWR} & land use and density, & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. (2020) [3]} & \multirow{4}{*}{Gradient Boosting regression trees (GMRT)} & \multirow{4}{*}{\begin{tabular}{c} socioeconomic \\ characteristics, land use \\ and density, rail \\ transit service \\ \end{tabular} } & \begin{tabular}{c} **O1 rdership in different \\ time periods of a day** \\ \end{tabular} & 800 m buffer zone & Metro & Nanjing, China \\ \hline [PERSON] et al. (2021) & GWR & \begin{tabular}{c} **-5D” of** \\ Built Environment \\ \end{tabular} & \begin{tabular}{c} Seasonal daily \\ traffic volume \\ \end{tabular} & 600 m buffer zone & Metro & Tai Pei, China \\ \hline [PERSON] et al. (2022) [17] & MGWR & \begin{tabular}{c} **-7D” of** \\ Built Environment \\ \end{tabular} & \
\begin{tabular}{c} Alighting ridership \\ during the morning \\ peak hours \\ \end{tabular} &
\begin{tabular}{c} Overlapped by 1000 m \\ radius circular buffer \\ zone and \\ Thessen polygon \\ \end{tabular} & Metro & Beijing, China \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} \\ \end{tabular} & 800 m grid distance & Metro & Xian, China \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Determining the PCA for a metro station is considered very important before conducting research [17]. Currently, most studies use circular buffers [1; 4; 16; 22; 23; 31; 34; 36; 52; 56] centered on the metro station as the PCA. However, considering that the station study areas overlap in areas with a dense distribution of metro stations, Tyson polygons [57] or Tyson polygons superimposed with circular buffers to take the intersection [17; 23; 24] have been used to determine the PCA. The radii of circular buffers chosen by different scholars varied widely, with more scholars choosing a circular buffer radii of 400 m [21], 500 m [31; 34; 600 m [22; 39; 52], 800 m [3; 4; 16; 23; 100; 1; 17], and 1000 m [1; 17]. In addition, the choice of buffer radius mostly relies on pedestrian accessibility [22; 32; 36], experience [4], and drawing on the research of others [23]. Existing research has shown that the PCA for metro stations varies from city to city (57) and that one cannot borrow the PCA for metro stations in other cities. Thus, the PCA of metro stations has been determined using the goodness of fit of regression models [17; 39]. Although some scholars have found the PCA via regression fit superiority methods, they tend to use a single PCA of metro stations. And, with the rapid development of cities, mega-cities like Beijing are establishing new districts on the outskirts of the city, which tend to be larger in scale. Therefore, using the uniform PCA of metro stations would make the error of the model larger.