## 4 Results and Discussion
### Factor Detection Results under Optimal Grid Scale and Optimal Discrete Method
Therefore, ride-hailing companies should pay attention to the allocation of vehicles in such areas during the morning peak hours. The FAR, residential POI density, and distance to the nearest subway station are the major contributors to the drop-off ridership in the morning peak hours, with contributions of 17.4%, 17.2%, and 17.2%, respectively. The reason why the FAR has a relatively high importance for drop-off ridership in the morning peak hours is that most destinations of the commuters in the morning peak hours are CBD areas with a high FAR. Therefore, the transport department should pay attention to the monitoring of traffic conditions in the areas with a high FAR during this period to avoid traffic congestion caused by more of the drop-off behavior of ride-hailing. In addition, temporary stops for ride-hailing vehicles can be located around the subway station to encourage residents to use ride-hailing services in an orderly manner. The FAR, residential POI density, and distance to the nearest subway station are the major contributors to the pick-up ridership in the evening peak hours, with contributions of 22%, 21%, and 15%, respectively. The distance to the nearest subway station may have had a relatively high importance for the pick-up ridership, because as a reservable travel mode, ride-hailing is a better choice for residents to solve the \"last 1 km\" problem [4]. The main influencing factors of the pick-up ridership in the evening peak hours were consistent with the ones of the drop-off ridership in the morning peak hours. The possible reason is that the pick-up locations in the evening were mostly the same as the drop-off locations in the morning. The FAR, residential POI density, and transportation POI density were the major contributors to the drop-off ridership in the evening peak hours, with contributions of 24%, 22%, and 17%, respectively. This is mainly because some people return to their places of residence after work, that is, residential areas with a high FAR. Another reason is that the POI of transportation facilities includes airports, railway stations, bus stations, and other transportation facilities. The people who use such facilities as destinations are mainly divided into two categories: one is the staff of such facilities, who  
Figure 8: Contribution of single explanatory variables on pick-up and drop-off ridership in the morning peak hours by the factor detector. as commuting tools, and the other is people who meet someone or drop them off at the station, so the stop-and-go mode of ride-hailing is a more suitable choice for them. In the four cases, the FAR and residential POI density were the major contributors to the ridership of ride-hailing, with the highest \(q\) values. The top three variables that were the major contributors to the ridership of ride-hailing drop-offs in the morning peak hours and pick-ups in the evening peak hours, in descending order of importance, were the FAR, the residential POI density, and the distance to the nearest subway station. The top four variables that were the major contributors to the pick-ups in the morning peak hours and the drop-offs in the evening peak hours, in descending order of importance, were the FAR, the residential POI density, the transportation POI density, and the public service POI density. For the same variable, the sizes of the two \(q\) values for pick-ups and drop-offs in the morning peak hours were significantly different, while the sizes of the two \(q\) values for pick-ups and drop-offs in the evening peak hours were less different. In other words, there were differences in the level of influence importance of the built-environment variables on the pick-ups and drop-offs during the morning peak hours. The possible reason is that ride-hailing destinations are relatively concentrated in some employment-intensive areas during the morning peak hours, while the starting points and destinations of ride-hailing trips during the evening peak hours are scattered.