## 5 Conclusions and Future Work  
Based on the order data of ride-hailing in Chengdu, this study used the NSR method and OPGD model to calculate the optimal grid scale and combined the \"5D\" built-environment influencing factors to build an impact factor model of ride-hailing in the morning and evening peak hours. By comparing the \(q\) value results of the geographical detector, the optimal data discretization method was found and the single-factor and interactive effects of the built environment on ride-hailing were analyzed. The main findings and conclusions are as follows: (1) The results of research on the impact of the built environment on the ridership of ride-hailing vary with different research scales. The average value of the calculated results of the NSR and OPGD model shows that the suggested grid scale for studying the impact of the built environment on the pick-up and drop-off ridership during the morning and evening peak hours in Chengdu is 1100 m. The proposed grid scale can provide a basis for the partitioning management and scheduling optimization of ride-hailing. (2) The factor detector results show that the FAR, the residential POI density, and the transportation POI density are the major contributors to ride-hailing ridership in the morning and evening peak hours. The interactive detection results show that the interactions of the diversity index of mixed land use \(\cap\) FAR, the distance to the nearest subway station \(\cap\) FAR, the transportation POI density \(\cap\) FAR, and the distance to the CBD \(\cap\) FAR made a higher contribution to the ride-hailing ridership than the single-factor effect of the FAR computed by the factor detector. The \"distance to the nearest subway station \(\cap\) FAR\" had the strongest interaction contribution, with a 135.3% increase in the \(q\) value compared to the single-factor effect of the FAR computed by the factor detector for the drop-off ridership in the morning peak hours. For the pick-up ridership in the evening peak hours, the interaction between the distance from the CBD and the residential POI density increased the contribution by 875% compared to the single-factor effect of the distance from the CBD, and the interaction between the transportation POI density and the distance to the CBD increased the contribution by 150% compared to the single-factor effect of the transportation POI density. For the drop-off ridership in the evening peak hours, the interaction between the distance to the CBD and the residential POI density increased the contribution by 1.233% compared to the single-factor effect of the distance to the CBD. Based on the results of the interactive detection, the effect of the interactions among built-environment explanatory variables on the demand for ride-hailing should not be ignored. In the process of adjusting the regional global ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables have important reference significance for formulating the priority renewal order of the built-environment factors. The interaction results for the explanatory variables of the built environment with relatively high contribution degrees provide a basis for proposing a scientific combination scheme of built-environment factors when adjusting the size of the demand for ride-hailing in a specific area. Although this study makes up for some gaps in the research investigating the built-environment impact on the ridership of ride-hailing, it still has the following limitations: (1) We took POI point data as the basic data, ignoring the impact of the actual built-up area of each influencing factor on the model results. In future research, the POI point density could be corrected through the area of interest (AOI) of the built environment to make the research results more real and accurate. (2) The amount of data obtained in this paper was limited.