Ensuring that commuting distance remains within a certain range has important effect of residents’ quality of life. Although many studies have investigated the relationship between the built environment and residents’ commuting distance, limited evidence has been provided of the impact of job location. As such, in this study, we used data from the Wuhan Metropolitan Development Area in China and applied Bayesian linear regression (BLR) models to examine the impact of the built environment at both residential and job locations on commuting distances for residents. Our findings showed that, for residential locations, the residential density, land use mix, number of intersections, parking service level, and number of companies have a significant negative effect on commuting distance, whereas the plot ratio, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. For employment locations, land use mix, parking service level, and number of companies have a significant negative effect on commuting distance, whereas job density, number of intersections, distance to sub-employment centers, number of metro stations, and number of bus stops have a significant positive effect on commuting distance. By describing the influence of the built environment at both residential and job locations on commuting distance, our findings are conducive to the optimization of land use and the formulation of related policies to reduce commuting distance, which has a positive effect on improving residents’ quality of life and reducing energy emissions and air pollution.

As the backbone of passenger transportation in many large cities around the world, it is particularly important to explore the association between the built environment and metro ridership to promote the construction of smart cities. Although a large number of studies have explored the association between the built environment and metro ridership, they have rarely considered the spatial and temporal heterogeneity between metro ridership and the built environment. Based on metro smartcard data, this study used EM clustering to classify metro stations into five clusters based on the spatiotemporal travel characteristics of the ridership at metro stations. And the GBDT model in machine learning was used to explore the nonlinear association between the built environment and the ridership of different types of stations during four periods in a day (morning peak, noon, evening peak, and night). The results confirm the obvious spatial heterogeneity of the built environment’s impact on the ridership of different types of stations, as well as the obvious temporal heterogeneity of the impact on stations of the same type. In addition, almost all built environment factors have complex nonlinear effects on metro ridership and exhibit obvious threshold effects. It is worth noting that these findings will help the correct decisions be made in constructing land use measures that are compatible with metro functions in smart cities.

Although many studies have investigated the non-linear relationship between the built environment and rail patronage, it remains unclear whether this influence is equally applicable to primary and secondary school students due to their physiological characteristics and cognitive limitations. This study applies the GBDT model to Wuhan student metro swipe data in order to investigate the relative importance and non-linear association of the built environment on the school-commuting metro ridership. The results show that the variable with the greatest predictive power is the number of living service facilities followed by the number of intersections, and the degree of land-use mixture. All of the built environment variables had non-linear associations with the school-commuting ridership, and the greatest attraction to the school-commuting metro ridership occurred when the number of living service facilities was 500, the number of intersections was 36, and the degree of land-use mixture was 0.8. These findings can help planners to prioritize land-use optimization and the effective range of land-use indicators when developing child-friendly rail transport policies.

Numerous studies have presented compelling findings regarding the favorable influence exerted by the construction of metro systems on the development of land use along the routes. However, the effects of metro network structure on land use change, especially the long-term dynamic effects, remain uncertain. To address this gap, this research utilizes land use coverage data and metro network data from Wuhan, China, spanning the period from 2014 to 2019. A complex network model is employed to analyze changes in the metro network structure, and the GTWR model is employed to investigate the impact of the of metro network structure on land use change. The results indicate that metro construction effectively stimulates land use change along the routes, predominantly leading to the conversion of non-construction land into construction land. Global accessibility, degree centrality, betweenness centrality, and eigenvector centrality, within the metro network structure attributes demonstrate significant positive effects on land use change, and these effects exhibit considerable spatiotemporal non-stationarity. Additionally, built environment factors surrounding metro stations, such as population density, plot ratio, street intersection density, and the quantity of bus stops, also exhibit notably spatiotemporal effects on land use change. These findings indicate that government agencies and urban planners should concentrate on optimizing the distribution of the urban metro network to improve its overall structural efficiency. When planning the construction of the metro network, careful attention should be paid to the built environment near the stations to effectively facilitate land use development in the vicinity.