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Exploring the impact of the urban built environment on online car-hailing, an increasingly popular mode of urban transportation, is crucial for developing corresponding transportation strategies and addressing sustainable urban planning. This paper captures that the form and orientation of the neighborhood network have a certain degree of influence on travel behavior. Therefore, the road network orientation entropy indicator is introduced based on the originally built environment elements, and the orientation-order (OO) indicator calculation model is constructed to normalize it. This study further tests and verifies its impact on the travel behavior of online car-hailing, thereby improving the factor characterization of neighborhood design latitude in the urban built environment. From the perspective of spatial correlation and spatial heterogeneity, the optimal models are selected by model comparative analysis, namely, the spatial Durbin model (SDM) and the mixed geographically weighted regression model (MGWR). Based on the optimal model estimation results, the influence mechanism of the urban built environment on the spatial-temporal distribution of online car-hailing travel in three cases (considering road network density (RD), considering RD and OO, considering OO) is compared and analyzed. The results show that the model fitting considering the OO is the best in the above three cases, and the OO has a significant impact on DiDi travel compared with the RD in this study, which verifies the rationality and necessity of selecting the OO in this study. In addition, catering service, corporate business, and OO have significant positive spillover effects, while the spillover effects of sports and leisure service and land-use mix are negative. The indicators of the bus station, residential district, catering service, shopping service, corporate business, land-use mix, life service, and OO have significant spatial heterogeneity.

Revealing the Influence Mechanism of Urban Built Environment on Online Car-Hailing Travel considering Orientation Entropy of Street Network