Abstract
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.
Highlights
In recent years, with the continuous expansion of the urban scale, it is vital to excavate the influence of the urban built environment on traffic travel
Based on the development background of big data technology, online car-hailing emerges as a new business model with its convenient and flexible booking service. e development of online car-hailing can integrate idle vehicle resources and improve the utilization rate of vehicles, which eases the difficulty of people taking taxis to a certain extent and conforms to the development concept of sharing economy
To verify the accuracy of the two factors of orientation order and road network density to characterize the neighborhood design under the data statistical condition of this study, the modeling and comparative analysis were carried out in three cases: in Case 1 (RD), road network density was used to characterize neighborhood design as one of the independent variables; in Case 2 (RD and Orientation order (OO)), both road network density and orientation-order indicator were put into the model as independent variables; in Case 3 (OO), the orientation-order indicator was selected
Summary
With the continuous expansion of the urban scale, it is vital to excavate the influence of the urban built environment on traffic travel. The rapid development of the emerging transportation mode will bring corresponding difficult challenges to urban traffic planning and management. Such challenges mainly include how to guide and manage the development of DiDi travel, how to integrate it into a variety of transportation systems (e.g., cars, buses, taxis, subways, and nonmotorized traffic), and how to Discrete Dynamics in Nature and Society reasonably integrate the built environment policies (e.g., regional development planning, comprehensive land development, and street network construction) with transportation policies (e.g., relevant policies such as bus, taxi, and online car-hailing operation management) [3], which are issues that researchers need to focus on at present
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