## 4 Results and Discussion
### Interactive Detection Results  
The interaction detector explores an interaction by comparing the \(q\) values of the interaction and the two single variables. In the study on the influencing factors of the ridership of rider-hailing in Chengdu, the interaction between two factors showed two kinds of relationship, namely nonlinear enhancement and double-factor enhancement. This indicates that the contribution of the interaction between two factors on the ridership of  
Figure 9: Contribution of single explanatory variables on pick-up and drop-off ridership in the evening peak hours by the factor detector. ride-hailing was enhanced to varying degrees compared with the single-factor effect, and the results are shown in Figure 10.  
Figure 10: _Cont._  
In the morning peak hours, the three interactions with the highest \(q\) values that affected the pick-up ridership were the diversity index of mixed land use \(\cap\) commercial POI density, the diversity index of mixed land use \(\cap\) residential POI density, and the transportation POI  
Figure 10: The results of the interactive detector for built-environment variables. (**a**) Pick-ups in the morning peak hours; (**b**) drop-offs in the morning peak hours; (**c**) pick-ups in the evening peak hours; and (**d**) drop-offs in the evening peak hours. density \(\cap\) building density, and the corresponding interactive \(q\) values were 0.40, 0.38, and 0.37, respectively. The interactions between two influencing factors (population density and diversity index of mixed land use degree) had relatively high interactive impacts on the pick-up ridership of ride-hailing. The three interactions with the highest \(q\) values that affected the drop-off ridership were the distance to the CBD \(\cap\) residential POI density, the distance to the CBD \(\cap\) distance to the nearest subway station, and the FAR \(\cap\) distance to the nearest subway station, and the corresponding interactive \(q\) values were 0.41, 0.40, and 0.40, respectively. The interactions between two influencing factors (distance to the nearest subway station and residential POI density) had relatively high interactive impacts on the drop-off ridership of ride-hailing. The traffic management department should pay more attention to the road traffic around the subway station and the areas with a high residential POI density in the morning peak hours to avoid traffic congestion caused by the concentration of drop-off demands. In the evening peak hours, the three interactions with the highest \(q\) values that affected the pick-up ridership were the FAR \(\cap\) distance to the nearest subway station, the distance to the CBD \(\cap\) residential POI density, and the diversity index of mixed land use \(\cap\) FAR, and the corresponding interactive \(q\) values were 0.41, 0.39, and 0.36, respectively. The interactions among three influencing factors (distance to the nearest subway station, population density, and residential POI density) had relatively high interactive impacts on the pick-up ridership of ride-hailing. The three interactions with the highest \(q\) values that affected the drop-off ridership were the distance to the CBD \(\cap\) residential POI density, the distance to the nearest subway station \(\cap\) FAR, and the transportation POI density \(\cap\) distance to the CBD, and the corresponding interactive \(q\) values were 0.40, 0.39, and 0.37, respectively. The interactions among three influencing factors (population density, distance to the CBD, and residential POI density) had relatively high interactive impacts on the drop-off ridership of ride-hailing. Urban planners should pay attention to adjusting the layout and scale of residential land and office land, strive to achieve a balance between work and residence, and improve urban traffic efficiency. The factor detector results showed that the FAR had the highest contribution to the ride-hailing ridership.