## 4 Results and Discussion
### Factor Detection Results under Optimal Grid Scale and Optimal Discrete Method  
The \(q\) value indicates the factor's explanatory power to the spatial distribution of the dependent variable [40]. The larger the \(q\) value, the stronger the contribution of factors to the spatial distribution of the ridership of ride-hailing. The factor detection results for morning peak hours and evening peak hours are shown in Figures 8 and 9, respectively. The FAR, residential POI density, and transportation POI density were the major contributors to the pick-up ridership in the morning peak hours, with contributions of 23%, 22%, and 19%, respectively. This may be because residents commute regularly on working days. In the morning, people leave the residential area to work, and the residential area is mostly distributed in clusters. Therefore, the demand conflict of pick-ups in the morning peak hours is mainly concentrated in places with a high FAR and residential POI density, and more ride-hailing vehicles should be allocated to these areas. In addition, as one of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Discretization Method**} & \multicolumn{4}{c}{**Pick-up Ridership in the Morning Peak Hours**} & \multicolumn{1}{c}{**Drop-off Ridership in the Park Hours**} & \multicolumn{1}{c}{**Pick-up Ridership in RidgeShip in the Park Hours**} \\  & & & **Pick-up Ridership in the Morning Peak Hours** & \multicolumn{1}{c}{**Brogship in Peak Hours**} & \multicolumn{1}{c}{**Ridership in the Park Hours**} \\ \hline \multirow{4}{*}{Population density} & NB & 0.11 & 0.07 & 0.10 & 0.10 \\  & EQ & 0.00 & 0.00 & 0.01 & 0.01 \\  & QU & 0.13 & 0.08 & 0.12 & 0.12 \\  & K & 0.10 & 0.07 & 0.09 & 0.09 \\ \hline \multirow{4}{*}{Building density} & NB & 0.14 & 0.12 & 0.11 & 0.12 \\  & EQ & 0.10 & 0.11 & 0.11 & 0.12 \\  & QU & 0.15 & 0.13 & 0.13 & 0.14 \\  & K & 0.12 & 0.10 & 0.11 & 0.12 \\ \hline \multirow{4}{*}{Transportation POI density} & NB & 0.22 & 0.10 & 0.17 & 0.21 \\  & EQ & 0.11 & 0.04 & 0.08 & 0.10 \\  & QU & 0.19 & 0.10 & 0.14 & 0.17 \\  & K & 0.18 & 0.07 & 0.12 & 0.15 \\ \hline \multirow{4}{*}{Semic spot POI density} & NB & 0.15 & 0.11 & 0.10 & 0.13 \\  & EQ & 0.09 & 0.03 & 0.04 & 0.03 \\  & QU & 0.13 & 0.12 & 0.11 & 0.13 \\  & K & 0.18 & 0.10 & 0.13 & 0.13 \\ \hline \multirow{4}{*}{Residential POI density} & NB & 0.22 & 0.18 & 0.21 & 0.21 \\  & EQ & 0.17 & 0.04 & 0.09 & 0.11 \\  & QU & 0.22 & 0.17 & 0.21 & 0.22 \\  & K & 0.23 & 0.15 & 0.19 & 0.21 \\ \hline \multirow{4}{*}{Public service POI density} & NB & 0.19 & 0.11 & 0.13 & 0.16 \\  & EQ & 0.04 & 0.00 & 0.02 & 0.04 \\  & QU & 0.17 & 0.10 & 0.14 & 0.16 \\  & K & 0.16 & 0.07 & 0.10 & 0.13 \\ \hline \multirow{4}{*}{Commercial POI density} & NB & 0.17 & 0.08 & 0.11 & 0.13 \\  & EQ & 0.05 & 0.01 & 0.01 & 0.02 \\  & QU & 0.14 & 0.07 & 0.08 & 0.10 \\  & K & 0.17 & 0.08 & 0.10 & 0.13 \\ \hline \multirow{4}{*}{Diversity index of mixed land use} & NB & 0.15 & 0.08 & 0.09 & 0.13 \\  & EQ & 0.08 & 0.02 & 0.05 & 0.07 \\  & QU & 0.13 & 0.10 & 0.09 & 0.10 \\  & K & 0.12 & 0.05 & 0.07 & 0.10 \\ \hline \multirow{4}{*}{FAR} & NB & 0.18 & 0.13 & 0.16 & 0.20 \\  & EQ & 0.21 & 0.13 & 0.19 & 0.21 \\  & QU & 0.23 & 0.17 & 0.22 & 0.24 \\  & K & 0.21 & 0.15 & 0.19 & 0.22 \\ \hline \multirow{4}{*}{Distance to CBD} & NB & 0.01 & 0.02 & 0.00 & 0.01 \\  & EQ & 0.00 & 0.02 & 0.00 & 0.00 \\ \cline{1-1}  & QU & 0.01 & 0.08 & 0.04 & 0.03 \\ \cline{1-1}  & K & 0.00 & 0.04 & 0.01 & 0.01 \\ \hline \hline \end{tabular} Notes: K represents the k-means clustering method; QU stands for the quantile method; EQ stands for the equal interval method; and NB stands for the natural break method.  
\end{table} 
Table 5: _Cont._important commuting tools, a ride-hailing vehicle is a substitute for or supplement to public transport [4, 68]. During rush hours, the transfer behavior of ride-hailing occurs in areas with a high POI density, such as bus and subway stations.