# The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
## 6 Conclusions  
This study utilizes ride-hailing data from Nanjing and integrates multi-source data including population, urban road network, POI, and public transportation stations to characterize built environment indicators from the perspective of the \"5 Ds\". By applying the XGBoost model, the study explores the impact of built environment factors on ride-hailing travel demand. Through the combined analysis of SHAP and PDPs, the study elucidates the contribution, nonlinearity, and threshold impacts of built environment features on predicting ride-hailing travel demand. The research findings provide valuable insights for urban development, transportation planning, and ride-hailing resource allocation. Firstly, the article estimated the average relative importance of the \"5 Ds\" and assessed the relative importance and ranking of various built environment features on ride-hailing  
Figure 6: Nonlinear effect of destination accessibility on ride-hailing travel demand. Figure 7: Nonlinear effect of distance to transit on ride-hailing travel demand. travel demand. The results indicate that the density dimension has the highest average relative importance at 7.89%, and the contribution rate of dining facilities to predicting ride-hailing travel demand is highest at 30.75%. Secondly, through the SHAP summary plot analysis, the article examined how built environment features positively or negatively affect the target variable. Based on the SHAP feature ranking, it was found that the top four built environment features are dining, finance, company, and medical facilities, all of which are POI facilities. TNCs should prioritize the supply and allocation of ride-hailing vehicles around these built environment features. Finally, through the PDFs plot visualization, the article illustrated the nonlinearity and threshold effects between built environment features and ride-hailing travel demand. The results indicate that built environment features need to reach a certain threshold or be within a certain range to exert the maximum impact on ride-hailing travel demand, with different thresholds and ranges for different built environment features. Analyzing the marginal impacts of population density, land use mix, and distance to subway stations revealed their comprehensive impact on ride-hailing travel demand. Understanding the nonlinearity and threshold effects between built environment variables and ride-hailing travel demand can help TNCs allocate ride-hailing resources reasonably and prevent overuse. The article has certain limitations, and future research could explore the following aspects: (1) The study focused on ride-hailing travel demand over a typical workweek without analyzing specific periods. However, ride-hailing travel demand typically exhibits peak and off-peak periods during the day, and demand characteristics may vary from weekdays to weekends [38]. Clarifying the nonlinear impacts of factors influencing ride-hailing travel demand during different periods is important. Future research could investigate how factors such as built environment features influence ride-hailing travel demand during peak and off-peak hours, both on weekdays and weekends. (2) Due to the similarity of temporal and spatial features in the demand for ride-hailing services within a certain period, this study only utilizes weekly trip volumes for analysis [38; 46]. In future research, we plan to employ more data to compare and analyze differences between weeks, thereby providing a more accurate reflection of the nonlinear threshold effects of the built environment on ride-hailing travel services. (3) Both built environment features and ride-hailing travel demand possess spatial attributes. Future research could analyze spatial effects in machine learning models. It could investigate whether interpretable machine learning models incorporating spatial effects outperform traditional geographically weighted models or exhibit similar or improved performance.