Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing.

Numerous machine learning models are viewed as an important means for evaluating the built environment (BE) features and travel behavior. However, most of them ignore the interaction effects of the BE and geographic locations. To strengthen their spatial interpretability, the study combines the random forest and GeoShapley method to scrutinize the nonlinear and spatial interaction effects of the BE features on ride-hailing demand using multi-source data from Nanjing, China. The results indicate that the land use mixture, the interaction between the distance to city center and geographic locations, and geographic locations are the most essential factors influencing ride-hailing demand. All BE features exhibit nonlinear effects on ride-hailing demand. Moreover, Among the BE features, distance to city center, land use mixture, and distance to metro stop demonstrate significant interaction effects with geographic locations. The findings indicate the necessity of incorporating geospatial analysis into the relationships and offer implications for implementing location-specific strategies.

The links between built environments (BE) and commute durations have been extensively studied. However, relatively few studies have considered the effects of BEs at different spatial levels within a unified framework, or identified the gendered relationships between BEs and commute durations. Using survey data from 3209 household couples in 97 Chinese cities, this study investigates the effects of neighborhood- and city-level BEs on commute durations and the potential differences in these effects between the male and female members of the same household couple. A multi-group generalized multilevel structural equation model is applied to reveal the gendered relationships between neighborhood- and city-level BEs and commute durations. The findings suggest that the BE variables at two levels have significant effects on the commute duration. The mediating roles that the traffic congestion, car ownership, and commuting modes play in linking these BEs and commute durations are confirmed. Both levels of the BE variables are more influential factors for males’ commuting durations. These findings have policy implications for the design of gender-equal transportation systems.

Considerable interest has been shown in decreasing car use through planning and policy-making efforts. However, little is known about the interactive effects of built environment (BE) and parking policy on car use, and it is unclear whether and how these effects differ across trip purposes. We conducted a web-based survey in Beijing and collected data from 1036 respondents, including 517 male and 519 female respondents. This study estimates the interactive effects of BE and parking policy on car use for home-based work and non-work trips by employing multilevel logit models. The results show that BE variables at trip origins and destinations are important for shaping car use for both home-based work and non-work trips. Specifically, land use mixture (Coeff. = −0.121), bus stop density (Coeff. = −0.006), and population density (Coeff. = −0.009) at residential locations are negative factors affecting car use for work trips, whereas distance to local center (Coeff. = 0.012) and distance to the city center (Coeff. = 0.019) at residential locations are positive factors. Land use mixture (Coeff. = −0.323), bus stop density (Coeff. = −0.008), road density (Coeff. = −0.002), and population density (Coeff. = −0.007) at residential locations are negative factors of car use for non-work trips. Among BE factors at destinations, land use mixture (Coeff. = −0.319), bus stop density (Coeff. = −0.015), road density (Coeff. = −0.008), distance to the local center (Coeff. = −0.018), and population density (Coeff. = −0.012) are negative factors for car use for work trips, whereas the negative factors for non-work trips are land use mix (Coeff. = −0. 218), bus stop density (Coeff. = −0.038), road density (Coeff. = −0.003), distance to the city center (Coeff. = −0.121), and population density (Coeff. = −0.009). The effects of BE variables can be strengthened or weakened by free parking and parking convenience. Moreover, the results identify significant differences in the effects between work and non-work trips. These findings inform planners and policymakers of how to coordinate the BE and parking policies to decrease car dependence.

Despite the rising interest in examining factors associated with body mass index (BMI), little longitudinal evidence can be found regarding its associations with the built environment (BE) and commute, which are closely related to sustainable urban development. Employing two rounds of nationwide survey data in China, this research assesses the links between BE, commute, and BMI in both cross-sectional and longitudinal ways. The findings indicate that non-motorized commute duration is negatively associated with BMI in both analyses. Switching from non-motorized to motorized modes is positively associated with BMI change, whereas switching in the opposite direction is negatively associated with BMI change. Among BE factors, increases in density, diversity, and green coverage are negatively associated with BMI change. Additionally, the research highlights the role of urbanization and suggests a nonlinear association with BMI that is consistent with an inverted-U pattern. The findings highlight the potential relevance of non-motorized commuting to overweight prevention and sustainable mobility. This research provides implications for integrating transport planning, public health, and sustainable urban development.