# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership  
[PERSON]  
1 College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China;  
2 [EMAIL_ADDRESS] (F.Z.); [EMAIL_ADDRESS] (J.M.); [EMAIL_ADDRESS] (W.T.); [EMAIL_ADDRESS] (J.Y.)  
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order data of one specific TNC (e.g., Didi or Uber) to represent ride-hailing demand [1; 4; 9]. However, data from a single TNC cannot depict a complete picture of urban mobility. In reality, multiple TNCs provide ride-hailing services within the same city; thus, analyses based on the order data of one specific TNC may lead to inappropriate policy implications. Moreover, ride-hailing has been introduced as an essential supplement to the traditional taxi industry; however, it is unclear whether taxi ridership has a competitive or complementary effect on ride-hailing ridership [10]. The debate may come from incomplete demand data in prior studies. Second, a considerable number of methods have been applied to explore the effects of the built environment on ride-hailing ridership [1; 9; 11]. Owing to the spatial variations of built environment effects, it is necessary to consider spatial non-stationarity to explore the relationship between the built environment and ride-hailing ridership. The geographically weighted regression (GWR) can capture the spatial changes in the built environment and effectively explain the spatial heterogeneity of the impact of built environment characteristics on ride-hailing ridership. Therefore, GWR usually outperforms global regressions [6; 11]. However, GWR assumes that built environment effects vary at a single spatial scale based on the same bandwidth; ignoring various built environment characteristics usually produces effects on diverse ranges of neighborhood environments [12].  
Aiming to fill the above gaps, this study explores the spatiotemporal relationship between the built environment and ride-hailing ridership, considering the effect of taxi ridership. This study contributes to the literature in two ways: (1) We collect demand data for traditional taxis and ride-hailing of all TNCs in Nanjing. Such data make the paper, to the best of our knowledge, the first attempt to explore the links between the built environment, taxi ridership, and ride-hailing ridership within an entire city. (2) We capture the spatiotemporal variations in the effects of different built environment variables on ride-hailing ridership based on a MGWR, which processes the independent variables in different bandwidths and their spatial variation effects. To characterize spatial contexts and obtain a precise relationship between the built environment and ride-hailing ridership, a multiscale GWR (MGWR) model, which can accommodate the spatial non-stationarity and the multiscale effect of each built environment variable simultaneously, is necessary. The findings provide policy implications for transport practitioners and urban planners on a finer scale.  
The rest of the paper is organized as follows. Section 2 reviews related studies on ride-hailing ridership, including data sources and the impact relationship between the built environment and ride-hailing ridership. Section 3 introduces the study area, data, and modeling approach. Section 4 presents and discusses the modeling results. At last, Section 5 concludes the study.