# The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
## 2 Literature Review  
### Impact of the Built Environment on Ride-Hailing  
In recent years, ride-hailing services have become increasingly pivotal in the overall transportation system. Research on the key factors influencing ride-hailing trips has received widespread attention, including social demographics [26; 27], user attitudes and preferences [28; 29], built environment characteristics [20; 21; 22], and other factors such as weather conditions [26]. Among these, the urban built environment, as a crucial factor affecting residents' travel behavior, is closely associated with ride-hailing trips. Built environment characteristics are commonly described using the \"5 Ds\": density, diversity, design, destination accessibility, and distance to transit [30; 31]. This framework has now been expanded to \"7 Ds\", encompassing demand management and demographic factors. Various aspects of the built environment, such as population density, land use patterns, and transportation infrastructure, are frequently studied to assess their correlation with ride-hailing trips. For instance, in exploring the linear relationship between the built environment and ride-hailing travel demand, [PERSON] et al. [32] utilized binary logit models to explore the joint and separate impacts of multiple factors on Uber/Lyft usage in California. The findings suggest that a diverse mix of land use and improved regional auto accessibility positively influence the utilization of ride-hailing services. Similarly, [PERSON] et al. [3] investigated the correlation between the intensity of ride-hailing trips and the density of Points of Interest (POI) using ride-hailing data from Chengdu, China, using ordered logistic regression models. They found that different types of POI had varying ramifications for ride-hailing trip intensity, with transportation facility density having the greatest impact, followed by scenic spot density, while company density had no significant influence. To examine the spatial correlation between the built environment and ride-hailing travel demand, [PERSON] et al. [33] explored the impact of the built environment on ride-hailing travel demand through multilevel modeling, utilizing Uber travel data from 24 distinct regions across the United States. The results indicated a positive correlation between Uber demand and land use mix and bus station density, suggesting that ride-hailing services complement the first/last-mile connectivity of public transit, a conclusion also reached by [PERSON] et al. [26]. Because of the differences in built environments and transportation preferences across cities, a higher land use mix leads to variations in demand for ride-hailing travel services. For example, in Chengdu, China, residents tend to walk to their destinations in areas with high land use mix, while in some cities in the United States, residents prefer using ride-hailing services to reach their destinations in areas with high land use mix. [PERSON] and [PERSON] [18] utilized Spatial Autoregressive (SAR) models and Structural Equation Models (SEM) to reflect spatial autocorrelation effects in census tracts. They compared the results with Ordinary Least Squares (OLS) models to explore how population and land use variables affect ride-hailing travel demand. The findings showed that an increase in job opportunities in retail and entertainment sectors promotes ride-hailing travel demand, while areas with pedestrian infrastructure and greater distance from public transit stations reduce ride-hailing travel demand. To capture the spatial heterogeneity of the built environment's influence on ride-hailing trips, [PERSON] and [PERSON] [21] utilized DiDi travel data from Chengdu, China, estimating both global models and Geographically Weighted Regression (GWR) models. They examined the spatial variation of factors influencing ride-hailing trips during morning peak, evening peak, and late-night periods.