This study used metro smart-card data from Wuhan, China, and explored the impact of the built environment on the metro ridership and station travel distance of elderly people using geographically weighted regression (GWR). First, our results show that elderly ridership at transfer stations is significantly higher than that at non-transfer stations. The building floor area ratio and the number of commercial facilities positively impact elderly ridership, while the number of road intersections and general hospitals has the opposite impact, of which factors show significant heterogeneity. Second, our results show that the average travel distance of terminal stations is significantly higher than that of non-terminal stations, and the average travel distance of non-transfer stations is higher than that of transfer stations. The distance of stations from the subcenter and building volume ratio have a positive effect, while station opening time and betweenness centrality have a negative effect. Our findings may provide insights for the optimization of land use in the built environment of age-friendly metros, help in the formulation of relevant policies to enhance elderly mobility, and provide a reference for other similar cities.

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time; (2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises; (3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy; and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

The long-distance commute to school caused by urban sprawl and the car-oriented urban construction model are key factors leading to primary/middle school students being picked up by their parents in cars. Encouraging those students to take rail transit can reduce their dependence on cars. This paper uses a stepwise regression based on rail-transit swipe data to explore the influence of the built environment on rail-transit commuting characteristics in Wuhan, and uses a geographically weighted regression (GWR) model to analyze the spatial heterogeneity of significant influencing variables. The study found that: (1) 60% of students are one-way commuters; (2) 88.6% of students travel less than 10 km; (3) the floor area ratio, bus station density and whether the station is a transfer station have an obvious positive effect on the flow of commuters; (4) whether the station is a departure station has a positive effect on the commuting distance, but the mixed degree of land use and road density have a negative effect on the commuting distance. This research can assist cities in formulating built environment optimization measures and related policies to improve school-age children’s use of rail transit. This is important in the development of child-friendly cities.

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.

The unique physical characteristics and travel preferences of disabled individuals may lead to specific spatiotemporal characteristics of subway travel. This difference may also be temporally heterogeneous on holidays versus every day, and its association with the built environment may be significantly different. However, few studies have investigated such differences in depth. This study explores the differences in the spatiotemporal characteristics of subway travel for people with disabilities using subway swipe data from Wuhan, China, on the May 1 holiday and every day, and analyzes the effects of the built environment on holiday and everyday travel for people with disabilities compared to non-disabled people using XGBoost and SHAP models. The results show that compared with non-disabled people, the spatial and temporal changes of passenger flow of disabled people are less affected by holidays, and the spatial distribution of passenger flow and traveling time is reduced and contracted, respectively, based on weekdays. Second, the relative importance of each element of the built environment on metro passenger flows for the disabled and the non-disabled differed significantly, with medical facilities and network density being the most important variables for the disabled, both on holidays and weekdays. Comparatively, non-disabled people are more affected by holidays, with the highest contribution to disabled metro patronage on weekdays being made by food and beverage facilities, which changes to attractions on holidays. In addition, all built environment elements show nonlinear and significant threshold effects on disabled and non-disabled metro ridership. Finally, the built environment has an interaction effect on disabled subway passenger flow, for example, the closer to the sub-center, the higher the number of firms, the more inhibitory effect on disabled weekday and holiday subway passenger flow. The results of the study will contribute to an in-depth understanding of the spatial and temporal characteristics of metro travel for among disabled individuals, thereby developing disability-friendly measures for rail transit systems.